{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27598 entries, 0 to 27597\n",
      "Data columns (total 7 columns):\n",
      "update_time      27598 non-null object\n",
      "id               27598 non-null object\n",
      "title            27598 non-null object\n",
      "price            27598 non-null float64\n",
      "sale_count       25244 non-null float64\n",
      "comment_count    25244 non-null float64\n",
      "店名               27598 non-null object\n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(r'c:/Users/WQ/Desktop/beautymakeup.csv',encoding=\"utf-8\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>update_time</th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>price</th>\n",
       "      <th>sale_count</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>店名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18164178225</td>\n",
       "      <td>CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜</td>\n",
       "      <td>139.0</td>\n",
       "      <td>26719.0</td>\n",
       "      <td>2704.0</td>\n",
       "      <td>自然堂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18177105952</td>\n",
       "      <td>CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品</td>\n",
       "      <td>194.0</td>\n",
       "      <td>8122.0</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>自然堂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18177226992</td>\n",
       "      <td>CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水</td>\n",
       "      <td>99.0</td>\n",
       "      <td>12668.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>自然堂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18178033846</td>\n",
       "      <td>CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25805.0</td>\n",
       "      <td>4287.0</td>\n",
       "      <td>自然堂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18178045259</td>\n",
       "      <td>CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜</td>\n",
       "      <td>139.0</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>自然堂</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  update_time            id                                  title  price  \\\n",
       "0  2016/11/14  A18164178225    CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜  139.0   \n",
       "1  2016/11/14  A18177105952   CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品  194.0   \n",
       "2  2016/11/14  A18177226992   CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水   99.0   \n",
       "3  2016/11/14  A18178033846  CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶   38.0   \n",
       "4  2016/11/14  A18178045259     CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜  139.0   \n",
       "\n",
       "   sale_count  comment_count   店名  \n",
       "0     26719.0         2704.0  自然堂  \n",
       "1      8122.0         1492.0  自然堂  \n",
       "2     12668.0          589.0  自然堂  \n",
       "3     25805.0         4287.0  自然堂  \n",
       "4      5196.0          618.0  自然堂  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.duplicated().sum()#计算重复条数\n",
    "df.drop_duplicates(inplace=True)#去除重复值\n",
    "df.shape#查看去重后数据条数\n",
    "df.reset_index(inplace=True,drop=True)#重置索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.any of        update_time     id  title  price  sale_count  comment_count     店名\n",
       "0            False  False  False  False       False          False  False\n",
       "1            False  False  False  False       False          False  False\n",
       "2            False  False  False  False       False          False  False\n",
       "3            False  False  False  False       False          False  False\n",
       "4            False  False  False  False       False          False  False\n",
       "5            False  False  False  False       False          False  False\n",
       "6            False  False  False  False       False          False  False\n",
       "7            False  False  False  False       False          False  False\n",
       "8            False  False  False  False       False          False  False\n",
       "9            False  False  False  False       False          False  False\n",
       "10           False  False  False  False       False          False  False\n",
       "11           False  False  False  False       False          False  False\n",
       "12           False  False  False  False       False          False  False\n",
       "13           False  False  False  False       False          False  False\n",
       "14           False  False  False  False       False          False  False\n",
       "15           False  False  False  False       False          False  False\n",
       "16           False  False  False  False       False          False  False\n",
       "17           False  False  False  False       False          False  False\n",
       "18           False  False  False  False       False          False  False\n",
       "19           False  False  False  False       False          False  False\n",
       "20           False  False  False  False       False          False  False\n",
       "21           False  False  False  False       False          False  False\n",
       "22           False  False  False  False       False          False  False\n",
       "23           False  False  False  False       False          False  False\n",
       "24           False  False  False  False       False          False  False\n",
       "25           False  False  False  False       False          False  False\n",
       "26           False  False  False  False       False          False  False\n",
       "27           False  False  False  False       False          False  False\n",
       "28           False  False  False  False       False          False  False\n",
       "29           False  False  False  False       False          False  False\n",
       "...            ...    ...    ...    ...         ...            ...    ...\n",
       "27482        False  False  False  False        True           True  False\n",
       "27483        False  False  False  False        True           True  False\n",
       "27484        False  False  False  False        True           True  False\n",
       "27485        False  False  False  False        True           True  False\n",
       "27486        False  False  False  False        True           True  False\n",
       "27487        False  False  False  False        True           True  False\n",
       "27488        False  False  False  False        True           True  False\n",
       "27489        False  False  False  False        True           True  False\n",
       "27490        False  False  False  False        True           True  False\n",
       "27491        False  False  False  False        True           True  False\n",
       "27492        False  False  False  False        True           True  False\n",
       "27493        False  False  False  False        True           True  False\n",
       "27494        False  False  False  False        True           True  False\n",
       "27495        False  False  False  False        True           True  False\n",
       "27496        False  False  False  False        True           True  False\n",
       "27497        False  False  False  False        True           True  False\n",
       "27498        False  False  False  False        True           True  False\n",
       "27499        False  False  False  False        True           True  False\n",
       "27500        False  False  False  False        True           True  False\n",
       "27501        False  False  False  False        True           True  False\n",
       "27502        False  False  False  False        True           True  False\n",
       "27503        False  False  False  False        True           True  False\n",
       "27504        False  False  False  False        True           True  False\n",
       "27505        False  False  False  False        True           True  False\n",
       "27506        False  False  False  False        True           True  False\n",
       "27507        False  False  False  False        True           True  False\n",
       "27508        False  False  False  False        True           True  False\n",
       "27509        False  False  False  False        True           True  False\n",
       "27510        False  False  False  False        True           True  False\n",
       "27511        False  False  False  False        True           True  False\n",
       "\n",
       "[27512 rows x 7 columns]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>sale_count</th>\n",
       "      <th>comment_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27512.000000</td>\n",
       "      <td>2.516200e+04</td>\n",
       "      <td>25162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>363.423512</td>\n",
       "      <td>1.231605e+04</td>\n",
       "      <td>1121.741197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>614.876153</td>\n",
       "      <td>5.241236e+04</td>\n",
       "      <td>5277.781581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.780000e+02</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>1.443000e+03</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>390.000000</td>\n",
       "      <td>6.353000e+03</td>\n",
       "      <td>669.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11100.000000</td>\n",
       "      <td>1.923160e+06</td>\n",
       "      <td>202930.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price    sale_count  comment_count\n",
       "count  27512.000000  2.516200e+04   25162.000000\n",
       "mean     363.423512  1.231605e+04    1121.741197\n",
       "std      614.876153  5.241236e+04    5277.781581\n",
       "min        1.000000  0.000000e+00       0.000000\n",
       "25%       99.000000  2.780000e+02      21.000000\n",
       "50%      205.000000  1.443000e+03     153.000000\n",
       "75%      390.000000  6.353000e+03     669.000000\n",
       "max    11100.000000  1.923160e+06  202930.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sale_count.mode()#sale_count的众数\n",
    "df.comment_count.mode()#comment_count的众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "update_time      0\n",
       "id               0\n",
       "title            0\n",
       "price            0\n",
       "sale_count       0\n",
       "comment_count    0\n",
       "店名               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0,inplace=True)\n",
    "df.sale_count=df.sale_count.astype('int64')\n",
    "df.comment_count=df.comment_count.astype('int64')\n",
    "df.isnull().sum()#检查是否完成缺失值的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\WQ\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.706 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>name_cut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜</td>\n",
       "      <td>[CHANDO, /, 自然, 堂,  , 雪域, 精粹, 纯粹, 滋润霜, 50g,  ,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品</td>\n",
       "      <td>[CHANDO, /, 自然, 堂, 凝, 时鲜, 颜肌活, 乳液, 120ML,  , 淡...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水</td>\n",
       "      <td>[CHANDO, /, 自然, 堂, 活泉, 保湿, 修护, 精华, 水, （, 滋润, 型...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶</td>\n",
       "      <td>[CHANDO, /, 自然, 堂,  , 男士, 劲爽, 控油, 洁面膏,  , 100g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜</td>\n",
       "      <td>[CHANDO, /, 自然, 堂, 雪域, 精粹, 纯粹, 滋润霜, （, 清爽型, ）,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   title  \\\n",
       "0    CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜   \n",
       "1   CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品   \n",
       "2   CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水   \n",
       "3  CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶   \n",
       "4     CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜   \n",
       "\n",
       "                                            name_cut  \n",
       "0  [CHANDO, /, 自然, 堂,  , 雪域, 精粹, 纯粹, 滋润霜, 50g,  ,...  \n",
       "1  [CHANDO, /, 自然, 堂, 凝, 时鲜, 颜肌活, 乳液, 120ML,  , 淡...  \n",
       "2  [CHANDO, /, 自然, 堂, 活泉, 保湿, 修护, 精华, 水, （, 滋润, 型...  \n",
       "3  [CHANDO, /, 自然, 堂,  , 男士, 劲爽, 控油, 洁面膏,  , 100g...  \n",
       "4  [CHANDO, /, 自然, 堂, 雪域, 精粹, 纯粹, 滋润霜, （, 清爽型, ）,...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jieba\n",
    "title_cut=[]\n",
    "for i in df.title:\n",
    "    j=jieba.lcut(i)\n",
    "    title_cut.append(j)\n",
    "df['name_cut']=title_cut\n",
    "df[['title','name_cut']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_type=[]#主类别\n",
    "second_type=[]#子类别\n",
    "basic_config_data = '''护肤品\t套装\t套装\t\t\t\t\t\t\t\n",
    "护肤品\t乳液类\t乳液\t美白乳\t润肤乳\t凝乳\t柔肤液'\t亮肤乳\t菁华乳\t修护乳\n",
    "护肤品\t眼部护理\t眼霜\t眼部精华\t眼膜\t\t\t\t\t\n",
    "护肤品\t面膜类\t面膜\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
    "护肤品\t清洁类\t洗面\t洁面\t清洁\t卸妆\t洁颜\t洗颜\t去角质\t磨砂\t\t\t\t\t\t\n",
    "护肤品\t化妆水\t化妆水\t爽肤水\t柔肤水\t补水露\t凝露\t柔肤液\t精粹水\t亮肤水\t润肤水\t保湿水\t菁华水\t保湿喷雾\t舒缓喷雾\n",
    "护肤品\t面霜类\t面霜\t日霜\t晚霜\t柔肤霜\t滋润霜\t保湿霜\t凝霜\t日间霜\t晚间霜\t乳霜\t修护霜\t亮肤霜\t底霜\t菁华霜\n",
    "护肤品\t精华类\t精华液\t精华水\t精华露\t精华素\t\t\t\t\t\t\t\t\t\t\n",
    "护肤品\t防晒类\t防晒霜\t防晒喷雾\t\t\t\t\t\t\t\t\t\t\t\t\n",
    "化妆品\t口红类\t唇釉\t口红\t唇彩\t\t\t\t\t\t\t\t\t\t\t\n",
    "化妆品\t底妆类\t散粉\t蜜粉\t粉底液\t定妆粉 \t气垫\t粉饼\tBB\tCC\t遮瑕\t粉霜\t粉底膏\t粉底霜\t\t\n",
    "化妆品\t眼部彩妆\t眉粉\t染眉膏\t眼线\t眼影\t睫毛膏\t\t\t\t\t\t\t\t\t\n",
    "化妆品\t修容类\t鼻影\t修容粉\t高光\t腮红\t\t\t\t\t\t\t\t\t\t\n",
    "其他\t其他\t其他'''#标准分类\n",
    "category_map={}\n",
    "for m in basic_config_data.split('\\n'):\n",
    "    basic_category_list=m.strip().strip('\\n').strip('\\t').split('\\t')\n",
    "    first_category=basic_category_list[0]\n",
    "    second_category=basic_category_list[1]\n",
    "    unit_category_list=basic_category_list[2:-1]\n",
    "    for unit_category in unit_category_list:\n",
    "        if unit_category and unit_category.strip().strip('\\t'):\n",
    "            category_map[unit_category]=(first_category,second_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'乳液': ('护肤品', '乳液类'),\n",
       " '美白乳': ('护肤品', '乳液类'),\n",
       " '润肤乳': ('护肤品', '乳液类'),\n",
       " '凝乳': ('护肤品', '乳液类'),\n",
       " \"柔肤液'\": ('护肤品', '乳液类'),\n",
       " '亮肤乳': ('护肤品', '乳液类'),\n",
       " '菁华乳': ('护肤品', '乳液类'),\n",
       " '眼霜': ('护肤品', '眼部护理'),\n",
       " '眼部精华': ('护肤品', '眼部护理'),\n",
       " '洗面': ('护肤品', '清洁类'),\n",
       " '洁面': ('护肤品', '清洁类'),\n",
       " '清洁': ('护肤品', '清洁类'),\n",
       " '卸妆': ('护肤品', '清洁类'),\n",
       " '洁颜': ('护肤品', '清洁类'),\n",
       " '洗颜': ('护肤品', '清洁类'),\n",
       " '去角质': ('护肤品', '清洁类'),\n",
       " '化妆水': ('护肤品', '化妆水'),\n",
       " '爽肤水': ('护肤品', '化妆水'),\n",
       " '柔肤水': ('护肤品', '化妆水'),\n",
       " '补水露': ('护肤品', '化妆水'),\n",
       " '凝露': ('护肤品', '化妆水'),\n",
       " '柔肤液': ('护肤品', '化妆水'),\n",
       " '精粹水': ('护肤品', '化妆水'),\n",
       " '亮肤水': ('护肤品', '化妆水'),\n",
       " '润肤水': ('护肤品', '化妆水'),\n",
       " '保湿水': ('护肤品', '化妆水'),\n",
       " '菁华水': ('护肤品', '化妆水'),\n",
       " '保湿喷雾': ('护肤品', '化妆水'),\n",
       " '面霜': ('护肤品', '面霜类'),\n",
       " '日霜': ('护肤品', '面霜类'),\n",
       " '晚霜': ('护肤品', '面霜类'),\n",
       " '柔肤霜': ('护肤品', '面霜类'),\n",
       " '滋润霜': ('护肤品', '面霜类'),\n",
       " '保湿霜': ('护肤品', '面霜类'),\n",
       " '凝霜': ('护肤品', '面霜类'),\n",
       " '日间霜': ('护肤品', '面霜类'),\n",
       " '晚间霜': ('护肤品', '面霜类'),\n",
       " '乳霜': ('护肤品', '面霜类'),\n",
       " '修护霜': ('护肤品', '面霜类'),\n",
       " '亮肤霜': ('护肤品', '面霜类'),\n",
       " '底霜': ('护肤品', '面霜类'),\n",
       " '精华液': ('护肤品', '精华类'),\n",
       " '精华水': ('护肤品', '精华类'),\n",
       " '精华露': ('护肤品', '精华类'),\n",
       " '防晒霜': ('护肤品', '防晒类'),\n",
       " '唇釉': ('化妆品', '口红类'),\n",
       " '口红': ('化妆品', '口红类'),\n",
       " '散粉': ('化妆品', '底妆类'),\n",
       " '蜜粉': ('化妆品', '底妆类'),\n",
       " '粉底液': ('化妆品', '底妆类'),\n",
       " '定妆粉 ': ('化妆品', '底妆类'),\n",
       " '气垫': ('化妆品', '底妆类'),\n",
       " '粉饼': ('化妆品', '底妆类'),\n",
       " 'BB': ('化妆品', '底妆类'),\n",
       " 'CC': ('化妆品', '底妆类'),\n",
       " '遮瑕': ('化妆品', '底妆类'),\n",
       " '粉霜': ('化妆品', '底妆类'),\n",
       " '粉底膏': ('化妆品', '底妆类'),\n",
       " '眉粉': ('化妆品', '眼部彩妆'),\n",
       " '染眉膏': ('化妆品', '眼部彩妆'),\n",
       " '眼线': ('化妆品', '眼部彩妆'),\n",
       " '眼影': ('化妆品', '眼部彩妆'),\n",
       " '鼻影': ('化妆品', '修容类'),\n",
       " '修容粉': ('化妆品', '修容类'),\n",
       " '高光': ('化妆品', '修容类')}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df)):\n",
    "    exist=False\n",
    "    for j in df['name_cut'][i]:\n",
    "        if j in category_map:\n",
    "            first_type.append(category_map.get(j)[0])\n",
    "            second_type.append(category_map.get(j)[1])\n",
    "            exist=True\n",
    "            break\n",
    "    if not exist:\n",
    "            first_type.append('其他')\n",
    "            second_type.append('其他')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['first_type']=first_type\n",
    "df['second_type']=second_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender=[]\n",
    "for i in range(len(df)):\n",
    "    if '男' in df.name_cut[i]:\n",
    "        gender.append('是')\n",
    "    elif '男生' in df.name_cut[i]:\n",
    "        gender.append('是')\n",
    "    elif '男士' in df.name_cut[i]:\n",
    "        gender.append('是')\n",
    "    else:\n",
    "        gender.append('否')\n",
    "#将“推荐男士使用”新增为一列\n",
    "df['gender']=gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>second_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18164178225</td>\n",
       "      <td>CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜</td>\n",
       "      <td>139.0</td>\n",
       "      <td>26719</td>\n",
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       "      <td>[CHANDO, /, 自然, 堂,  , 雪域, 精粹, 纯粹, 滋润霜, 50g,  ,...</td>\n",
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       "      <td>2016/11/14</td>\n",
       "      <td>A18177105952</td>\n",
       "      <td>CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品</td>\n",
       "      <td>194.0</td>\n",
       "      <td>8122</td>\n",
       "      <td>1492</td>\n",
       "      <td>自然堂</td>\n",
       "      <td>[CHANDO, /, 自然, 堂, 凝, 时鲜, 颜肌活, 乳液, 120ML,  , 淡...</td>\n",
       "      <td>否</td>\n",
       "      <td>护肤品</td>\n",
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       "      <td>2016/11/14</td>\n",
       "      <td>A18177226992</td>\n",
       "      <td>CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水</td>\n",
       "      <td>99.0</td>\n",
       "      <td>12668</td>\n",
       "      <td>589</td>\n",
       "      <td>自然堂</td>\n",
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       "      <td>否</td>\n",
       "      <td>护肤品</td>\n",
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       "      <td>2016/11/14</td>\n",
       "      <td>A18178033846</td>\n",
       "      <td>CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25805</td>\n",
       "      <td>4287</td>\n",
       "      <td>自然堂</td>\n",
       "      <td>[CHANDO, /, 自然, 堂,  , 男士, 劲爽, 控油, 洁面膏,  , 100g...</td>\n",
       "      <td>是</td>\n",
       "      <td>护肤品</td>\n",
       "      <td>清洁类</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016/11/14</td>\n",
       "      <td>A18178045259</td>\n",
       "      <td>CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜</td>\n",
       "      <td>139.0</td>\n",
       "      <td>5196</td>\n",
       "      <td>618</td>\n",
       "      <td>自然堂</td>\n",
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       "      <td>否</td>\n",
       "      <td>护肤品</td>\n",
       "      <td>面霜类</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "  update_time            id                                  title  price  \\\n",
       "0  2016/11/14  A18164178225    CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜  139.0   \n",
       "1  2016/11/14  A18177105952   CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品  194.0   \n",
       "2  2016/11/14  A18177226992   CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水   99.0   \n",
       "3  2016/11/14  A18178033846  CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶   38.0   \n",
       "4  2016/11/14  A18178045259     CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜  139.0   \n",
       "\n",
       "   sale_count  comment_count   店名  \\\n",
       "0       26719           2704  自然堂   \n",
       "1        8122           1492  自然堂   \n",
       "2       12668            589  自然堂   \n",
       "3       25805           4287  自然堂   \n",
       "4        5196            618  自然堂   \n",
       "\n",
       "                                            name_cut gender first_type  \\\n",
       "0  [CHANDO, /, 自然, 堂,  , 雪域, 精粹, 纯粹, 滋润霜, 50g,  ,...      否        护肤品   \n",
       "1  [CHANDO, /, 自然, 堂, 凝, 时鲜, 颜肌活, 乳液, 120ML,  , 淡...      否        护肤品   \n",
       "2  [CHANDO, /, 自然, 堂, 活泉, 保湿, 修护, 精华, 水, （, 滋润, 型...      否        护肤品   \n",
       "3  [CHANDO, /, 自然, 堂,  , 男士, 劲爽, 控油, 洁面膏,  , 100g...      是        护肤品   \n",
       "4  [CHANDO, /, 自然, 堂, 雪域, 精粹, 纯粹, 滋润霜, （, 清爽型, ）,...      否        护肤品   \n",
       "\n",
       "  second_type  \n",
       "0         面霜类  \n",
       "1         乳液类  \n",
       "2         化妆水  \n",
       "3         清洁类  \n",
       "4         面霜类  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水</td>\n",
       "      <td>99.0</td>\n",
       "      <td>12668</td>\n",
       "      <td>589</td>\n",
       "      <td>自然堂</td>\n",
       "      <td>否</td>\n",
       "      <td>护肤品</td>\n",
       "      <td>化妆水</td>\n",
       "      <td>1254132.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-14</th>\n",
       "      <td>A18178033846</td>\n",
       "      <td>CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25805</td>\n",
       "      <td>4287</td>\n",
       "      <td>自然堂</td>\n",
       "      <td>是</td>\n",
       "      <td>护肤品</td>\n",
       "      <td>清洁类</td>\n",
       "      <td>980590.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-14</th>\n",
       "      <td>A18178045259</td>\n",
       "      <td>CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜</td>\n",
       "      <td>139.0</td>\n",
       "      <td>5196</td>\n",
       "      <td>618</td>\n",
       "      <td>自然堂</td>\n",
       "      <td>否</td>\n",
       "      <td>护肤品</td>\n",
       "      <td>面霜类</td>\n",
       "      <td>722244.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                                  title  price  \\\n",
       "update_time                                                               \n",
       "2016-11-14   A18164178225    CHANDO/自然堂 雪域精粹纯粹滋润霜50g 补水保湿 滋润水润面霜  139.0   \n",
       "2016-11-14   A18177105952   CHANDO/自然堂凝时鲜颜肌活乳液120ML 淡化细纹补水滋润专柜正品  194.0   \n",
       "2016-11-14   A18177226992   CHANDO/自然堂活泉保湿修护精华水（滋润型135ml 补水控油爽肤水   99.0   \n",
       "2016-11-14   A18178033846  CHANDO/自然堂 男士劲爽控油洁面膏 100g 深层清洁  男士洗面奶   38.0   \n",
       "2016-11-14   A18178045259     CHANDO/自然堂雪域精粹纯粹滋润霜（清爽型）50g补水保湿滋润霜  139.0   \n",
       "\n",
       "             sale_count  comment_count   店名 gender first_type second_type  \\\n",
       "update_time                                                                 \n",
       "2016-11-14        26719           2704  自然堂      否        护肤品         面霜类   \n",
       "2016-11-14         8122           1492  自然堂      否        护肤品         乳液类   \n",
       "2016-11-14        12668            589  自然堂      否        护肤品         化妆水   \n",
       "2016-11-14        25805           4287  自然堂      是        护肤品         清洁类   \n",
       "2016-11-14         5196            618  自然堂      否        护肤品         面霜类   \n",
       "\n",
       "                   销售额  日期  \n",
       "update_time                 \n",
       "2016-11-14   3713941.0  14  \n",
       "2016-11-14   1575668.0  14  \n",
       "2016-11-14   1254132.0  14  \n",
       "2016-11-14    980590.0  14  \n",
       "2016-11-14    722244.0  14  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['销售额']=df.sale_count*df.price\n",
    "df['update_time']=pd.to_datetime(df['update_time'])\n",
    "df=df.set_index('update_time')\n",
    "#新增日期号数为一列\n",
    "df['日期']=df.index.day\n",
    "del df['name_cut']\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rIgYB7wHOKPfZlWL4UCvFXIhdgb8vq/87xUsLby8f5Oewcff8JYr5S9+ta1vngNIP2DNXfg9Mn051ejXYb0VZfyIwsyw7giKs9cvMpaw82b0vxQIIu1AMP/sQxRvpv1QuKLEvxRC3AygWlNjY37Xa71I/iuFq/1iep6VTnRaKJbth5d+L2RS/Xy/UhsmVK4mdXbf8dVCEl6w73psoei5fpFiW/FsU88cOopjnVPtZt1P83g2mmL+zE8V8sJo9y3ZOyswZndq5xpUXJMkAI6kn60vxgNdo/H99neV1XwNsUTf34IcUD8aNtu1L+Z6XiNiO4gGtNvm7N43/2t/S6V/K/cdQPAgeCIylmCtyQGb+rq7O2yh6dI6h+Et355dZ3k0xDKiV1x462ymCQ20Z5d4U9+SD1M1jiYgby7Jp5TyG64FfZebaemD2Ls/xI4oH4psp3s1yFkWweU9mLgDIzHkR8R7gpxShbhwbcc/LFci+U3ctW1D0YNQ7kZXn8BzDyr1klPv0jvJlkGXZExTB5fa6erVVtRqtWtanvN6k6C3ZLzM7Fo7IzL9G8e6W2s9lg9tdJyju5UfLYYi1e1BvIsVCFvdTvvw0Ij4MnEsxbLG9rrdkELBNRHRe/WuLiPifzByXmc+Vv6tP1VaAi+JdRd8HzsjMW8t9HqCY77WIYn7Oezod88vAt8reqM76N2iHJHWIcqi1JPU45epU22dmoxW8uvpcbwI+n5n/XH5/OnBsZh7Sqd4oivdebF/30Fnryfg+xUpoP6hNiu60b1+KCdd/AL6xpqWVN+D6R1L8f8LT5ffHAjMbXUeDfd+Qmf9T3u83UwSYQ4FtM/MHDervBfxldUsTq+uUPTBbd/e9joi/W81KY5LU5QwwktQNyof5PqsbJiZJkjaMAUaSJElSZbSsvYokSZIkbR4MMJIkSZIq43W5Clm/fv1y6NChm/oyJEmSJAHz5s1rzcx+61L3dRlghg4dyty5czf1ZUiSJEkCImLButZ1CJkkSZKkyjDASJIkSaoMA4wkSZKkynhdzoGRJEmSukt7ezu+a3FlEUFLS9f0nRhgJEmSpC7Q2trKnDlzWL58+aa+lM1Snz59GD58OH379t2o4xhgJEmSpC4wZ84cBg8ezDbbbENEbOrL2axkJgsXLmTOnDnsuuuuG3UsA4wkSZK0kdrb21m+fDnbbLMNvXv7iN3INttsw4svvkh7e/tGDSdzEr8kSZK0kWpzXux5Wb3avdnY+UEGGEmSJEmVYYCRJEmSVBkGGEmSJKnJTj31VA466CDGjRsHwPPPP8+nPvWpjTrmxRdf3C11NzfxelyjetiwYTl37txNfRmSJEnqIdra2njmmWcYOXIkvXr1Wmv9nXfemT//+c9deg0jRoxg9uzZXV63q6zpHkXEvMwcti7HsQdGkiRJapIbbriBcePGsWDBAsaNG8f5558PwOzZszt6Y2rGjRvH1KlTOeigg7j88ssB+MMf/sCBBx7IQQcdxJFHHsnixYs7jvn888+vdMw1nb9z3aOOOopf/vKXAHz3u9/lk5/8JPfeey/77rsvRxxxBGPGjOGHP/whAH/+85+ZOHEi48aN433vex9Lly7t6tu0RvbASJIkSRtpfXtgOveAzJ49mxNOOIF77723o2zcuHFss802fO9736N///4AXHTRRfzlL3/hwgsv5Gc/+xmjRo1i5513bnjM9Tn/bbfdxtSpU7nuuus47LDDuOSSS1iwYAGnnnoqv/3tb3nppZd4+9vfzty5czn++OOZNGkSJ5xwAp/61Kd461vfyimnnLJR98geGEmSJKkH+PznP98RXgA+9rGP8dJLLzF+/Hh+8IMfsNVWW3XJeSZOnMjDDz/MzJkzWbFiBXvssQcAY8aMoU+fPgwdOpRBgwaxcOFCfve733H55Zczbtw47rvvPp5//vkuuYZ15Vt2JElSJY0+64YuPd5jFx7fpceTusKWW2650vf33HMPp59+OqNHj+aUU07hhz/8If/8z/8MFC/ThOI9K2t7H03nui0tLXz0ox/l2GOP5dxzz+2o98gjj9Da2spLL73EkiVL2Hbbbdlzzz05/fTTOfjgg7nvvvtYtGhRF7Z47eyBkSRJkipi1KhRnH766RxyyCE88cQTjB8/vmPbSSedxNixY9lvv/3WepxGdWu9Ox/84Ac7yt7whjdw9NFHM378eC699FJaWlr46le/ygUXXMAhhxzCOeecw1ve8paubeRaOAdGkiRVkj0w2pys7xyYzc3tt9/O5MmTOfHEEzvms9x7771cc801XHPNNV1yjq6aA+MQMkmSJKmHmTlzZsP3ynzzm99k9913X6V80qRJTJo0aaWycePGrbIy2ubAACNJkiT1MGPGjFlpRbOexDkwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiS9zvzlL39Zp3p33HEHra2t3Xw168dJ/JIkSVI36OqlvmvWd8nv8ePHM2XKFEaOHAlAa2sr7373u7n99tsZMWIEAF/84he5+uqrGTBgAHvssQdTp04FinfDNHr9yOOPP87BBx/MLrvs0lGWmfTp04df//rXG9iydWOAkSRJknqwPn360KdPHwDuv/9+Zs2axXve8x5uvvlmhgwZwqRJk+jTpw+TJ09mzz335JJLLmHJkiW0tLQwZMgQ2tvbWbx4Mf369es4zoABAxg/fnxH0AF49dVXV3qxZncxwEiSJEk90J133sn555/PU089xYc+9CEOOugglixZwsiRI5k4cSIAF154IbvuuisR0bFfS0sLH/3oR5kxYwYLFy5kxIgRrFixgu9973tMmDABKHpbGlldeVcywEiSJEk90IQJE5gwYQJHHXUUU6ZMYcSIEZx22mnss88+HS+ovP766+nduzdtbW184QtfYMCAAeyzzz786Ec/AmDPPffk8ccfX+XYK1as4OGHH2bPPfdcqXy33Xbr9nYZYCRJkqTXgRdeeKFhD0ktwNSGkE2ZMoWvf/3r9OnThxdffJEpU6aQmey6665MmjQJgFGjRjFr1qxmN6G43k1yVkmSJEnd6k9/+hM//elPeeqppzjmmGM47rjjWLx4MWeccQYDBw4E4KmnnuLUU09l0aJFXHvttQwYMIAxY8Zw6KGH0t7ezvPPP8/222/P7rvv3rHP4Ycfzvz58+nVqxdz5szhvvvu45xzzuGZZ56hb9++LFy4kMmTJ/Ov//qv3dIuA4wkSZLUA/33f/83bW1tbLfddtxwww2MGDGCww8/nB//+McMHz4cKJZJHj58OPPmzeOKK65g4MCBTJkyhTFjxjBt2jRGjx7NI488wgc/+MGO4/bt25d77rmHbbfdlhNOOIGBAwfSt29fbrvtNkaMGMFll13WMdm/OzTlPTARMTgifhwR90fEAxHxtogYGREPRsSMiDi5ru4nIuKxiJgeEbuVZf0j4tZy/8ujnGUUEWMj4tHyGMc0oy2SJElSFYwdO5ZTTz2VrbfeGoC2tjb++Mc/stNOO3XUmThxIjvssANPP/00o0aN6ihfsWIFkydP5qqrrmLatGk8/fTTHdtaWlaOEBHRsKy7NKsH5uPAw5l5fkRMBM4FRgBnA/cAP42I24GtgNOAfYFhwCXAJOC8cv/PRcT5wIcj4kbgemAiMBu4LyJ+mplLm9QmSZIkabXW930t3e2uu+5i9OjR9OrVa6XyZ599lpdeeonM5K677mKLLbbgjDPOYO+992bvvffmK1/5ChMnTmTq1Knsv//+tLe3c+ihh3YMITvnnHNob2/nyCOP7BhCdt5553VbO5rSAwM8D4yKiIHAfsCTwE6ZOT2LmUR3AocChwE3ZebSzJwF7BgRvYAJwHXlsW4BjgD2AOZl5tOZuQx4EHhHo5NHxJkRMbf2Wbx4cTc2VZIkSdo8fO5zn+ORRx6hX79+fPrTn+ZTn/rUKnVeffVVPvvZz7J8+XJmzpzJiBEjePDBB/mv//ovoOilOffcc/nxj38MFC/CvOeee5g5cyYf+tCHWLZsGa2trdx2223MnDmTa6+9tuMFmd0hmrFWc0S8EbgMeIAiZHwduCAz31Vu/1dga6APMD8zv12WzwDeD9wPvC0zWyNiJEXPzBeBUzPz+LLul4DfZuZU1mLYsGHZ6I2ikiSpOrr6Leeb21/LVS1tbW0888wzjBw5cpUejk3p6aef5o1vfCMDBgzg7rvv7lhFbG1WrFhB795dO1hrTfcoIuZl5rB1OU6zhpBdCJybmY9FxADgCWBR3fbBQJRlA9dQ3roOdSVJkiTBSvNa1jW8AF0eXrpSs4aQDQT2Lr8+BPgbQETsUJbtCzwLzKAYRkZEbAlsDyyoL6+r+3tg94jo36lckiRJUg/VrGj1BeDKiPgG8BfgVGA7YFpEPACMAU7MzCUR0RIR1wBvAa7IzBXlfj+JiP2B44CjyuFkVwK3R8RcoI1iHowkSZKkHqopASYzZ1L0kKwkImYCewFnZ+aSsvhoipXFlmbm9HL/pyLiIIqJ/pdm5uyy/IKImA4MB6ZlMyb0SJIkSdpkNungtsx8kmJFsvqyNuDWBnXnA6tM0M/Mh4CHuusaJUmSpKpbvnz5er9ccunSpfTv379b3+myITbf2TmSJElShT1//qi1V9oA23/u6bVXqjN37lwmTpzIzJkzefbZZznxxBP5xS9+0bDuW9/6Vp566ikApkyZQltbG2efffYq9R5//HEOPvhgdtlll46yzKRPnz78+te/Xq/rW1/NmsQvSZIkaROYMmUKH//4x+nduzd9+/alpaWIAJnJlClTmD9/PjfeeCMAW265Zcd+f/jDH3jnO9/Z8JgDBgxg/Pjx/OY3v+n43H///fTt27fb22OAkSRJknqoefPmcemll/L+979/lW0XXnghN998M/379+fCCy/kV7/6FS0tLbS1tZGZzJo1i3322Qcows6KFSs69l3d1PNmTEl3CJkkSZLUA7W3t3PSSSfRu3fvVV4cefnll3PzzTdz1113MWjQIK6//nouvvhiAK6++mquvfZaHn/8cY466igWLVrECy+8wHHHHcdXv/pVoHjR5cMPP8yee+650nF32223bm+XAUaSJEnqgW666Sa23XZbxo4du1L5o48+SktLC3fccQeDBg0CYOTIkXzzm9/kgAMO4KSTTmLs2LFcdNFFXHnllVx77bUsWrSIT37ykx3HGDVqFLNmzWpqe2ocQiZJkiT1QB/4wAe46qqrOr5/4YUXOP3009l555254447VprvAsVcmZqf/exnTJo0CYC7776bgw8+uGPb4Ycfzl577cWYMWPYeuutefLJJ3nf+97H7rvvzpgxY9h555359re/3W3tsgdGkiRJ6qFqSyfff//9nHPOOZxyyiksXrx4lcn2V111Fc8991zH90OGDOHSSy+lvb2dOXPmMHr06I5tffv25Z577mHbbbflhBNOYODAgfTt25fbbruNESNGcNlll633ks3rwwAjSZIk9XD7778/M2bMYPny5dx5550d5X/605+YM2cOV111Fffee29HT8vHPvYxjj76aPbdd18GDhzI448/3jHfpbaKWU1ENCzrLgYYSZIkqRus7/tauktm0qtXL3baaSfmzJnDX//6144XW15yySUA3HDDDfTt25dXX30VKJZQ/sxnPsNJJ53EEUccwbHHHsv555/PscceS3t7O4ceeii9evVizpw5nHPOObS3t3PkkUfSt29fFi5cyHnnnddt7THASJIkST1YW1sbra2tAOy4447ssssu7LfffkDx3perr76aESNGAPDKK6/w0ksvcfrppzN58uSOBQBmzJjBK6+8AkBra2vHELJTTjmFZcuW0dra2jGEbPr06d3anmjGWs2bm2HDhuXcuXM39WVIkqSNMPqsG7r0eI9deHyXHk+vL21tbTzzzDOMHDlylSWLVVjTPYqIeZk5bF2O4ypkkiRJ0kbqzjkfPc3G3isDjCRJkrSRIoKIYPny5Zv6UjZby5cv77hPG8M5MJIkSdJGigiGDBnC/Pnz2WmnneyR6SQzmT9/PkOGDDHASJIkSZuD7bbbjueee26TvaF+c9e/f3+22267jT6OAUaSJEnqAi0tLbz5zW+mvb2d1+NCWWvS6F0xG8oAI0mSJHX6Nss4AAAgAElEQVShrnpQV2PeXUmSJEmVYYCRJEmSVBkGGEmSJEmVYYCRJEmSVBkGGEmSJEmVYYCRJEmSVBkGGEmSJEmVYYCRJEmSVBkGGEmSJEmVYYCRJEmSVBkGGEmSJEmVYYCRJEmSVBkGGEmSJEmVYYCRJEmSVBkGGEmSJEmV0ZQAExH/EhH31n1eiYhDIuLBiJgRESfX1f1ERDwWEdMjYreyrH9E3BoR90fE5RERZfnYiHi0PMYxzWiLJEmSpE2nKQEmM6/KzHGZOQ74NHAT8BXgbGAscGxEDI+IvYDTgAOAk4FLykOcBzycmWOB+cCHI6IPcD1wHHAoMDkitmhGeyRJkiRtGptiCNl/UgSXnTJzemYmcCdFCDkMuCkzl2bmLGDHiOgFTACuK/e/BTgC2AOYl5lPZ+Yy4EHgHY1OGBFnRsTc2mfx4sXd2kBJkiRJ3aOpASYiDgOeKb+dU7fpZWAHYHCn8iXA0E7lq6tbK19FZl6UmcNqn0GDBm1sUyRJkiRtAs3ugfk34FvAImBgXflgINaxfG11JUmSJPVQTQswETEU2CUzH83Ml8uyWo/JvsCzwAyKYWRExJbA9sCC+vK6ur8Hdo+I/p3KJUmSJPVQvZt4rknA9LrvLwCmRcQDwBjgxMxcEhEtEXEN8BbgisxcERHfAH4SEftTTNo/KjNbI+JK4PaImAu0UcyDkSRJktRDNS3AZOZ1vDYRn8y8ISJmAnsBZ2fmknLT0cBEYGlmTi/rPhURB1FM9L80M2eX5RdExHRgODCtXBBAkiRJUg/VzB6YVWTmk8CTncragFsb1J0PTG1Q/hDwUHddoyRJkqTNx6ZYRlmSJEmSNogBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlNDXARMQJEXFl+fXIiHgwImZExMl1dT4REY9FxPSI2K0s6x8Rt0bE/RFxeUREWT42Ih4tj3FMM9siSZIkqfmaFmAiYhfgfwNnlkXfBc4GxgLHRsTwiNgLOA04ADgZuKSsex7wcGaOBeYDH46IPsD1wHHAocDkiNiiWe2RJEmS1HxNCTAR0UIRNp4APhYRWwM7Zeb0zEzgTooQchhwU2YuzcxZwI4R0QuYAFxXHu4W4AhgD2BeZj6dmcuAB4F3rOb8Z0bE3Npn8eLF3dhaSZIkSd2lWT0wHynP9X8oQszDwJy67S8DOwCDO5UvAYZ2Kl9d3Vr5KjLzoswcVvsMGjRooxskSZIkqfl6N+k87wAuz8w5wJyIeAn4u7rtg4EAFgED11Deug51JUmSJPVQzeqBeRIYBRARQ4HtgbkRUesx2Rd4FphBMYyMiNiyrLegvryu7u+B3SOif6dySZIkST1Us3pgvgN8KyJ+DmxHMXl/OTAtIh4AxgAnZuaSiGiJiGuAtwBXZOaKiPgG8JOI2J9i0v5Rmdlarmh2e0TMBdoo5sFIkiRJ6qGaEmAy81XgnzqXR8RMYC/g7MxcUhYfDUwElmbm9HL/pyLiIIqJ/pdm5uyy/IKImA4MB6aVCwJIkiRJ6qGa1QPTUGY+STG8rL6sDbi1Qd35wNQG5Q8BD3XXNUqSJEnafDT1RZaSJEmStDEMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqo2kBJiL+EBH3lp//iIihEfHLiLgvIs6vq/f3EfFYue2Asiwi4jsR8UBE3BgRW5TlIyPiwYiYEREnN6stkiRJkjaNpgSYiNgVmJmZ48rPZ4EpwHcy80BgRES8KyK2Ay4GDgPeC1xSHuIUoFdm7g/cDpxRln8XOBsYCxwbEcOb0R5JkiRJm0azemAOBvaOiPsj4lcRsR9FSPl+uf0W4AjgXcAvM/OvmfkisCAi3gRMAK6rrxsRQ4CdMnN6ZiZwJ3Boo5NHxJkRMbf2Wbx4cbc1VJIkSVL3aVaAeQwYn5ljgXOArwKvZubScvvLwA7AYGBO3X6NytdWdxWZeVFmDqt9Bg0a1DWtkiRJktRUvZt0nicy89Xy60eAtwFL67YPBgJYBAxcS/na6kqSJEnqoZrVA3NNREwov/4H4DfA4xGxb1m2L/As8ABwSDlpvxcwGvgTMINiyFlH3cx8GSAiduh0DEmSJEk9VLN6YCYDUyPiq8Bcikn5I4DrIuIO4APAOzPz+Yj4FfCj8truK8uuAX5RhpX3AaeXx70AmBYRDwBjgBOb1B5JkiRJm0BTAkxmzgL26VT8XEQcCRwAXJCZ88u6p0bEu4EtKFYcIzMXlEsqTwCuzswnyvIbImImsBdwdmYuaUZ7JEmSJG0azeqBaSgzZwOzG5RPb1D2MvCDBuVPAk92w+VJkiRJ2sw07UWWkiRJkrSxDDCSJEmSKsMAI0mSJKkyDDCSJEmSKsMAI0mSJKkyDDCSJEmSKsMAI0mSJKky1ivARMTta9h2ycZfjiRJkiSt3lpfZBkRewLLy8+giNgZeCvQVu7/GPARYI9uvE5JkiRJWnuAAe4r/50OvARMAj4J7Ai8DPwT8D5gcXdcoCRJkiTVrMsQsoeA3wIP1JV9uSybCqyg6I2RJEmSpG61PnNgch2+liRJkqRusy5DyGqC18LKscCIcv8dgDcDi7r0yiRJkiSpkw1dRvllikn9S4AXgdbye0mSJEnqNusbYKL896fAPOAR4Efl1/O78LokSZIkaRUbMwfGuS+SJEmSmmpd5sC8i6LnZRFF4AngS8A2wNuAO9fxOJIkSZK0UdYleIwEllEEl5sphoz9hmLeS3/gT8BtwPhuukZJkiRJAtYhwGTmvNrXEdEnMxcACzpV+8+IGNDVFydJkiRJ9dYaYCLiI5n5vYgI4LiI2IlV578k8PnuuEBJkiRJqlljgImIocAZEfHvwEeAhyhWG9sBeL7u38HAbGCf7rxYSZIkSa9va1yFLDMXZOa+wJcpgsuMzNwFmJmZbwZ+n5kjKObJvCkiBnf3BUuSJEl6/VpbD8z1FJP1Ad7Da0PHVhpClpkLI2J4Zr7S9ZcoSZIkSYW1zYGZBrwKtANbAP/SaXtHkDG8SJIkSepuaxtCNhV4DjgZmAG8OSLOAYaV/24fEefUPt1/uZIkSZJez9YYYCJiW4pemJuAvwLfAZYD/0Xxbpivlf+2UrwnRpIkSZK6zRqHkGXmXyNid2CXzFwKfAkgIt4BtGbmzIh4Q2b+TxOuVZIkSdLr3Bp7YEpbAN8FiIjfREQfYBxwYET0Be6JiNO67xIlSZIkqbC2Vci+CMwCVpRFg4ELgL3Lsl3K7RdHxOLMvLYbr1WSJEnS69zaViFbSPEOmOXlfJgW4HfAGynmwjxWfn5L0VMjSZIkSd1mbXNgLoqIbwHfAB4GLgauAwYAS+1xkSRJktRMa+uBoZy8f2JEHJaZd5fFl3bvZUmSJEnSqtZlEj8AdeFlo0TE1Ig4ISKGRsQvI+K+iDi/bvvfR8Rj5bYDyrKIiO9ExAMRcWNEbFGWj4yIByNiRkSc3BXXJ0mSJGnztc4BpitExIeAY8pvpwDfycwDgRER8a6I2I5imNphwHuBS8q6pwC9MnN/4HbgjLL8u8DZwFjg2IgY3pyWSJIkSdoUmhZgImJ74CzgW2XRYcD3y69vAY4A3gX8MjP/mpkvAgsi4k3ABIq5Nx11I2IIsFNmTs/MBO4EDl3Nuc+MiLm1z+LFi7ujiZIkSZK6WTN7YC6j6Dn5W/n9q+X8GoCXgR0olmmeU7dPo/K11V1FZl6UmcNqn0GDBnVBcyRJkiQ1W1MCTET8C/D7zLyvrrit7uvBQACLgIFrKV9bXUmSJEk9VLN6YP4eOCgi7gVOoJi3sm1E7Ftu3xd4FngAOKSctN8LGA38CZhBMeSso25mvgwQETt0OoYkSZKkHmqtyyh3hcw8qvZ1RJwLzKYIJtdFxB3AB4B3ZubzEfEr4Efltd1Xll0D/KIMK+8DTi8PdwEwLSIeAMYAJzajPZIkSZI2jaYEmHqZeW7t64g4EjgAuCAz55fbT42IdwNbUKw4RmYuKJdUngBcnZlPlOU3RMRMYC/g7Mxc0tTGSJIkSWqqpgeYepk5m6I3pnP59AZlLwM/aFD+JPBkN1yeJEmSpM1MU98DI0mSJEkbwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTJ6b+oLkKTuNPqsG7r0eI9deHyXHk+SJK0fe2AkSZIkVYYBRpIkSVJlGGAkSZIkVYYBRpIkSVJlNC3ARMTAiDg8IvZs1jklSZIk9SxNCTARsQXwM+BdwMUR8W8RMTIiHoyIGRFxcl3dT0TEYxExPSJ2K8v6R8StEXF/RFweEVGWj42IR8tjHNOMtkiSJEnadJrVA/N24EuZeT5wBnA08F3gbGAscGxEDI+IvYDTgAOAk4FLyv3PAx7OzLHAfODDEdEHuB44DjgUmFwGJUmSJEk9VFMCTGY+kJm3RMQo4DMU4WWnzJyemQncSRFCDgNuysylmTkL2DEiegETgOvKw90CHAHsAczLzKczcxnwIPCORuePiDMjYm7ts3jx4u5sriRJkqRu0uxJ/BOAtwEJzKkrfxnYARjcqXwJMLRT+erq1spXkZkXZeaw2mfQoEFd0BRJkiRJzda7mSfLzEsi4g6KHpdFdZsGA1GWDVxDees61JUkSZLUQzVrEv9pEfGF8tutgefK8lqPyb7As8AMimFkRMSWwPbAgvryurq/B3aPiP6dyiVJkiT1UM3qgfkOcH1E3A8so5ioPxqYFhEPAGOAEzNzSUS0RMQ1wFuAKzJzRUR8A/hJROxPMWn/qMxsjYgrgdsjYi7QRjEPRpIkSVIP1ZQAk5mvAO/vVPxERMwE9gLOzswlZfnRwERgaWZOL/d/KiIOopjof2lmzi7LL4iI6cBwYFq5IIAkSZKkHqqpc2A6y8wngSc7lbUBtzaoOx+Y2qD8IeCh7rpGSZIkSZuPZq9CJkmSJEkbzAAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqwwAjSZIkqTIMMJIkSZIqo/emvgBJktR8o8+6oUuP99iFx3fp8SRpdeyBkSRJklQZBhhJkiRJlWGAkSRJklQZBhhJkiRJlWGAkSRJklQZBhhJkiRJlWGAkSRJklQZBhhJkiRJlWGAkSRJklQZBhhJkiRJlWGAkSRJklQZBhhJkiRJldGUABMRgyLiJxFxV0Q8FBH7RcTIiHgwImZExMl1dT8REY9FxPSI2K0s6x8Rt0bE/RFxeUREWT42Ih4tj3FMM9oiSZIkadNpVg/MR4HvZ+Z44BzgP4DvAmcDY4FjI2J4ROwFnAYcAJwMXFLufx7wcGaOBeYDH46IPsD1wHHAocDkiNiiSe2RJEmStAn0bsZJMvNbdd9uD/w3sEdmTgeIiDspQsgbgJsycykwKyJ2jIhewATgg+X+twCfAJ4E5mXm0+UxHgTeAfy88/kj4kzgzNr3W221Vdc2UJIkSVJTNHUOTEQMBT4DXAPMqdv0MrADMLhT+RJgaKfy1dWtla8iMy/KzGG1z6BBgza+MZIkSZKarmkBJiL6Av8XmAzMBAbWbR4MBLBoHcrXVleSJElSD9WsSfy9gKnArZl5Y2a+XJbXekz2BZ4FZgCHldu2pBhutqC+vK7u74HdI6J/p3JJkiRJPVRT5sAA/wIcCWwdEUdThJILgGkR8QAwBjgxM5dEREtEXAO8BbgiM1dExDeAn0TE/hST9o/KzNaIuBK4PSLmAm3Ag01qjyRJkqRNoFmT+L8NfLtzeUTMBPYCzs7MJWXx0cBEYGltkn9mPhURB1FM9L80M2eX5RdExHRgODAtM7PbGyNJkiRpk2lWD0xDmfkkxWpi9WVtwK0N6s6nGIbWufwh4KHuukZJkiRJm4+mrkImSZIkSRvDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMgwwkiRJkirDACNJkiSpMno382QR0Qf4MfCVzLw3IkYC1wMrgGsz8/Ky3ieAk4GFwMmZOSsi+gP/DxgCPA6ckpkZEWOBKcBS4MuZ+ZP1va7RZ93QBa17zWMXHt+lx5MkSZJUaFoPTET0BW4BhtcVfxc4GxgLHBsRwyNiL+A04ACKEHNJWfc84OHMHAvMBz5cBqLrgeOAQ4HJEbFFM9ojSZIkqfmaPYTsROA3ABExBNgpM6dnZgJ3UoSQw4CbMnNpZs4CdoyIXsAE4LryOLcARwB7APMy8+nMXAY8CLyj80kj4syImFv7LF68uJubKUmSJKk7NC3AZGZrZs6tKxoMzKn7/mVghwblS4ChncpXV7dW3vncF2XmsNpn0KBBG9scSZIkSZvAppzEvwgYWPf9YCDWsXxtdSVJkiT1QJsswGTmywARUesx2Rd4FphBMYyMiNgS2B5YUF9eV/f3wO7lBP/6ckmSJEk9UFNXIWvgAmBaRDwAjAFOzMwlEdESEdcAbwGuyMwVEfEN4CcRsT/FpP2jMrM1Iq4Ebo+IuUAbxTwYSZIkST1Q0wNMZp5Q9/UNETET2As4OzOXlJuOBiYCSzNzeln3qYg4iGKi/6WZObssvyAiplOsbjatXBBAkiRJUg+0qXtgyMwngSc7lbUBtzaoOx+Y2qD8IeCh7rpGSZIkSZuHTTmJX5IkSZLWiwFGkiRJUmUYYCRJkiRVhgFGkiRJUmUYYCRJkiRVhgFGkiRJUmUYYCRJkiRVhgFGkiRJUmUYYCRJkiRVhgFGkiRJUmUYYCRJkiRVhgFGkiRJUmUYYCRJkiRVRu9NfQGSJEmbg+fPH9Wlx9v+c0936fEkFeyBkSRJklQZBhhJkiRJleEQMm12Rp91Q5ce77ELj+/S40mSJGnTMcBI3cAQJvVc/vctSZuWQ8gkSZIkVYYBRpIkSVJlGGAkSZIkVYZzYCRJTfP/t3feYXpV1R5+f5NGCYQSpIUQOgpIEQQFNCpIR7iCICBdQIqFjogiICKCSgcB6dIUQZBegqiEdgEpwlWEUES61JBC1v3jtw9z8mVmMknmm+Z6nydP5tRv73N2WW2vk+tHkiRJklklPTBJkiRJkiRJkvQZUoFJkiRJkiRJkqTPkApMkiRJkiRJkiR9hlRgkiRJkiRJkiTpM6QCkyRJkiRJkiRJnyEVmCRJkiRJkiRJ+gyZRrkfkelJkyRJkqTvkfN3kswYqcAkSZIkSZIkCalM9hUyhCxJkiRJkiRJkj5Dn/fASPoRsBHwb2CniHilh4uUJEnS5aRVMEmSJElMn/bASNoI+DSwOnAC8KOeLVGSJEmSJEmSJM2kr3tgNgAuiYgpku4ATurpAiVJfyKt/kmSJElnyTkj6S4UET1dhplG0rnA5RFxc9n+Z0Qs2cZ5+wP713YthEPOmsVQ4J0m3r+7yHr0HvpDHaB/1KM/1AGyHr2J/lAH6B/16A91gKxHb6I/1AG6px4LRMSQzpzY1z0wbwFz1rbnbuukiPgZ8LNuKREg6fmIGNFdv9cssh69h/5QB+gf9egPdYCsR2+iP9QB+kc9+kMdIOvRm+gPdYDeV48+vQYG+AvwBQBJywG5gD9JkiRJkiRJ+jF9XYG5FlhL0qnAFcDPe7g8SZIkSZIkSZI0kT4dQhYR70taB6dRvjAi7u3pMhW6LVytyWQ9eg/9oQ7QP+rRH+oAWY/eRH+oA/SPevSHOkDWozfRH+oAvawefXoRf5IkSZIkSZIk/1309RCyJEmSJEmSJEn+i0gFJukzSOqxkEdJ6qnfTnoGSUN7ugxJ0haS5ujpMrSFpBZJs/d0OZKOkdSpNLVJ0ptJBaYLkLSApEG17RZJs/VkmSokbS9p8dr2VZJWbOfcr0n6SPn7AUlLd3DfkyQtXP6+RdKy5e8DJH1mFsv8VDXASjpW0grl0HUl2xySlpK0Y8N1h0v6Zjv3fEnSQ238e09Sm+m3a9cOBV6WNHpW6tXVSJqn9vfC3fi7LZIGtHNMHSmakmafUcWgKxRXSb+UtHn5+3ZJD0oaW/69UtbS1c9fE3ikvXq2cf9LJK03nXOWk3RzbXvcTFRleuUYUJVZ0kFVmSR9XdIWtXMGSvqSpBG1a6+Q9MWuLtOs0BnDgaSW2t+qtstY1GMpPyWdLGn1Tp47QtLN7c0bknaTdEz5ewjwf11Y1OmVrcN6SDpa0u5lcxngljbOGSlpVPm3jKTZJN0haXlJFzWp3FtLWqoZ9+4NlDnwiw379pW0Uhvn3i9pyfL3AODlbipm0+gJRblZhkxJ81X3l7RQM36jg98eLOkiSWMk3SNpFUnvlGMLSfq7pDVq549pHA8kjZZ0XXeWG/r4Iv7uQtLOwIsRcVM7p5wI3AmcW7bnA26WdCRwf0T8q3avk4DjIuJFSbcA+0TE/0k6ALgvIv7YxcX/B3CFpO8AKwMrAOdImgzMC7wXEWvIFr0jgavKdZOAyR3c91LgLGDzcu4kSfMDewLnzGKZ3wdmByaU39lO0q+BFyLiyXLOv4AjJP0FOAYYBSwITJH0VeCdiFi/ds+nI2KtakPSbCUJxP3Ae7X9hwKbls2zI+ICYCfg0VK3MbNYt6koA+LS+BkCDACmAD8FLgEWjYhTy7k7A3dGxNNF0LlOVuImA1dKWjsiOnpnXcWngO9J+qC2fX+pQwtwD/DD6uTSD96MiJ8D2+P39KP6DYvQuQ3+rtPwcs5iwEhgNknrRcSsTLqTaX3GE4EtI+KZ8tvnA2uX/+sf6ZoNeKhWz7mAH0TExW3cv93+Iuk3pR6DgFGSxpZDC5S/W4D/jYi9Zq5qU7E+cEAp8yjgLUkHAiOAiZL2wuP+OTjt/JWSfgAsAqwGXCDpDfwcRgEjI+L9LijXzLJ3GVfOwM/4+8BDwO+BwcB/gB9IWhdYBXgYuLoIcZsCK0v6ICJ2aWYhJT0O/G/ZnAv4FrA2cHDDeevh/hL4vT8ZEZcDa+Ex6v3auQsCfwDexf1htpqiPVzSGPwuH+2itjMj9VgVj//v4XbyvqQdcJ9ZvpRtEHBTRByFE+0sUOr+MnBHKfcTkv4iaU5gQhePXzsBd5fy3obHlur+q+L5bxBwErBXREyWdHQp21rApRHxdK3OI2k1+g4CngNuAL4BHB4RX+vCsneGycApkvaMiDFF8D0E+F0p7+exbPIBsDzwO0nVGDhHmfsAbouIQ7q57JQyLgUsVX2MvOzbF89zj5R5YUBETJL0U9yebpWNYDcC65Q5dGBETGrzR7qWPWSD2pkR8UH9QCmrqv2S9gTmj4hjZcPrKRExjYFI0qLAbZJWweP07yR9MiLGN702Zlvg3YgYXRTig0q5BgO/BY6IiPu6qSwzRCownWNj2knRXDTnrYA5Ja0PnA+MxQP788BlkjaoNcZmCv7TEBH3SPoCnli2xJP89cDx+P1PLKd+FQ/wtxUjwwq4I00AhgDXRsT3i9XjjnJNSxHAlsOKz5zAMOAmSY9FxG4zUtYyKI3Gk/sPJT2BBV7K/+9IOjoijoiI8ZK2BF6KiG3L9QdixeXMth5FGdAXK0rJ4ZIeKb9VH4gWB75HUYgkDQMOBNbFwt3mEfH7GanXdBgA7IiVte2AX+GJ/lIsvHy9DJhT8IR5o6RdgOPwV3HPwwrzB8DtknaLiL93YfmmopRlJeCa2u5lgZuxoFWd91HgwrJvJG7nXwIWBgaVvjIIuCoiToyIKcXq8zoWUL8N/Coirp/F8m4MXInb+k6SXgUeaef030TEoR3c61TcjyqlZzn83AfhZ/AZSZWxYnbgZxFxSURsVa4ZCXwzIg4s21dExFdmpX6NRMSNuI18H7gJuBfYBKecfwJYOiLOqtXpLmw1XwO/15uxQPc3YLUeVl4AdgB2Bn6Cx8tP4Of+aTwu/TgiDgeQNDYi1pW0PP4+2IblHud3Qzn/DXw7Il4tbTGRbGMAABfISURBVPstrHTcWMbT1YBPAk/jMfdyrLC/IekF3Lcp4+lHsPB8OLB62f85YH78Ln8N7BoRX+2pekTEg6U+SPoK8BJwBBagt4iI7zTc96Fy7S24bf0UGFzqOxz4Hzz/Xd6FdVkgIp4vf08EtomIV0uZH42IiZK2BV4uyouArUvZBgAfK+9mUjjbUU8oYW0iR3w8i9tQ5XE5GjgsIl4ox+8E1ih1uwfP8ctHxPWSXo2I1UudezISp0MlDI9NZ0iaiMfYz5Z5vgW/nxsBYZnrB91Q3s3xeHKnbAReEngbG4NagBMkHYvnveHAQNnzP4RW49Ug4PSIqAzeJ+N5pfJgjAeuLf1tEO6PDzaxTs8B60n6bFEkby5z8enA3RFxWRN/e5bILGTtIGkx4DHg8TYOL4wnzjMlHYcb2a+xEvJbbMG6EvgcnkjvxALUh4I/FkiXA56hVfB/GphhwX869RiIO9y1EXG5pE2AvYE5sDX5j0Up+SswOiJeKNeNBbatrNS1+7UAT0XEEh385nDgurrHo5NlXRP4GlZiNgY2ANaOiJ3L8aWxZf8a4LtY2D0MWwcn4wFuMvAUfsaPFusfxdq0NlYKDgJOwdbqMcBaZYKqhNTfYAVmAazwjcOC4GPl/O0jYsyM1G069d4LT/CHYmXpr2V7OLYwvw3sHRFPSpozIt4t73E+rJT+Awvl/4qI27qqXO2UdUD5rXqo3on4vbxVtr+In/2F5ZpDgdci4mxJOwELR8RxDfe9DfejKbivLIXfb/Vx2iHABe0opx2VdyAWRp4E9gFux31zQaw0gifJzbDR4VO4H9eFkAG47x6HhZm3G37jbNxWpwCbRcQ0CpLsQfwYU3t4wMLqeRHx4xmpV3uU/vkT3F4Xx2PRHri//wq3sduAv0XExbL3ZSjuR/tjT8yCeMy4JSKmdEW5ZgZJa2MlcE1JV+H+uBhuZ29gY8UmsuX/G1iQewzX/xhaBYLtImJkk8t6H8WzV373VPxuT8ZK7l8jYoXa+c9ExKjy91MRsVTt2F7AQhFxpBwSuxWthqYVgRdw/VuAuYFPR0R1vNvqIenHuL9UfWUNPDa8j/vMpMriLOkQ/G6WxkLfssCtWPBcj+KxrcbhWazDR3G6108Cd+H+vh2wU12Bwe3llnLsZDyHXwA8gPvquFK3HSJiXJmfVsNj0YdKGB6jh+P5+5ziUWsqks4sZWmrfwoYVzeOSPoEfv4XYi/m8Ij4U7PL2RFFyZqMI0NWioiLylh6ZxmbBtW9KpI2w/LYD4F98VjblPDDdsq7BHBZRKxZ23cZcHREPNbG+VsAc0TEr+UQ70Mj4lsN5+yDZZFdsBFgFWwsPQX3739GxBPNqlOtHF/Ac8MwbDB6DLfxF2LqSBZk7+qBEXF/bd/osm9TupH0wLTPBBzWMbrxQLEATCiDwqbAfngAfB9baX6CO+UfcSPcBoc8LdgZwb+rKlCEt+uxJfZpOf7+BezNGAb8SNJpWGA/t1JepsMQHKKxPfbiPNdwfCSepK6ciSJ/FocijY6IZyX9GSsdU1UrIq7AYXHjsLC/L63hQeDOfynwp4brJkjaFb+Xc4sXp7KgtyUALI4niCPwRLgN9pZ8Q9JdjS7kWWCzUl6A3fEz2B9bkP9UfnvJMmkdLek94M1SturvV4G3JakrhIAOGIAn6hF4oPs9FqbmwUrNI6XMdave7HhwBvhMROxWLH9zRMS7ABHxBfgwpOAS7Al4E7+bb85seECxPi6O3+Xm+N0DbB1Th5BNiYhHJW2IhZAPFaUyOO8cEa833l92/38cK0bjsOeoLQPEpFKnZxv2r8nUbXdW+RS2om+Klb9j8PMX8HUsmJ4OHCvpXCx8PYiVmgsi4nhJ8wJHAb+StE9EXN2F5esURRE7Hhgvr9k7B7f3HfDE+oBP06eKsHM5DhfaFVgC+CetnpeNuqHIbwLHAn8GfoGV4tPxWpVP42ePvJ5vV2BeSSfgMJh2+2tEnFz6/U7FAHAZcCYW5A6JiAN6oh6lbIeVtrJxRFxSjF47YIV40wYjxQp43lkaG6DOwW1zKyz8j8YhX12xLuw5POc9i40gc+Px6LpiNQe3+63Lb3+nXLMv8HBErFfGhOOwsa7qn6NpVcI2wXP6NEpYdxCdDBmUtDd+ZxNqu8/HnoGBWCA/rq1ru4FTqClhRZgHWEnSfsC44iF7Fc/zdf4ILCxpN2AhHJZ/fpPLewT2jlRLAVbGoYgLFW/Jw5WCIuk5Slsu74Dy90vYQ3m3HHGyNTaqLoDH6PmwsWkh3GYrA17TkLQM8FBErC9HLFyN28sawK2StoyI33V4kx4iFZj2mQKsKqktK8WieLAaBxyOrUlXAMtGxLeLgrMIcFRE3AIfLjhrluDfJkV4+xZ2LX8Fe3rmxpNmxa7AI+H1ID/D2v8HtIaQTcTxpZ8o9xwPfEfSNthL0FjeoyPiPWyVn1F+jb0NB5btCcAISf/BLvtJTB1iEEUJuQGHV0zBwsBI3PleBdYqiuF/yjVv4EFo/rL9TnkubSkwz+BJalfgYmxl/wD4aldZpougNgpbzJfAMdVDsUI8HAvdL0bEl4rlY2v8jLbDz2MVbCFdF1sDT8YDUFMolt5NijXsVuDFcuj5sk3dAlkmok2wUg8WgsDCyzK4XVbWuG2xMWAHrAxdhdvhA8VTcH1E1CfizrIzFswWwOGb0+Ngeb1Rxdz4/bTFqTh0YVvspTlU0qYR0WiIEPYCPdmwf+FyrKv4Cw7HWQkLLi/ifjEFK8UTgFeKF++EiPhbaYNL4HZFRLwB7Cevy+spF/2++FktifvBMrjvVeuHlsYT/AuS7sVemYexkPwoXr/xi3Luf+ge/ohDQd6NiCPgQ6PUa9G6DvJZvFZyGyzEv1bOq88zCwEXFyV/UDjUaXNJ19bO2RK4p5wzeCb7xUzXo7SZAXj83E1e51WxDXBDOWdgREyMiB0lfR3YC/fB4aX+Y4H1IuLIrip8RLwjL1i/FBuHnsRj4/rAsRGxX3luy+C1eAdjw9EgHGZ6K/bALIe9+TuUW3enEtYh9bbRxrGB2CAzJSJOxwpodWwojkD4Kl5X1nRvUXt0RgkrbehSbLB4D7+jMXjc3BuHu64INHWNRjFiLU2rx2slrKRXi92HA/VQq/EN2xU7UAxW5drRxSixBTaAz4fn8x3w+HYvTa4b9v68gw0XD+G+P7nMEd8BLpF0Q/R8OPE0pALTMQ924IEhHCd8HbYYr4QXlM2HBd518SR0b0S82WTBv12KgPKxiPhYKft+2MI8vliZVDXMiNi/Vsc2Q8hqjKV17cih+Bk8h0M5Zrasz5ffrrafkvRlPMjeCrzajpXli3jC+QeOPT0PhwL8uRxfEfirnL3nMmxxO0yOlX8OT2R1AfUU7Gn6JbaA/AcPXJ8DRkXEeTNbx0aKIrSCpItxQoWz5DC2b1I8MFHC1SLiMkmDi0BzPbbU7A08HhHnShpaDajNpEwqD2Bvy3gsuI+nlhmpKCTHYE/AfaVtg9cinY0F4+3LuQOxd+IVrGB+F3smlsbehG/iuOhRtLMWrYOyzgN8GYd2XomFkPVpXd8FDlc7tXbZ8W15YNq49w+B1yPixmIpBIdr3SqJBiVmHJ4owN6rt7FSJeDsGanTdNgTP9vXsVK+Wdl/GU7ScSRwnxyXfZikd/Fi5kWAJ4u18KNYCZgNe9PaXRfURM7FXr1LwrHxX8ZjatW+Z8ft72rsta4LcqOwBb6iRdLO3WChPRQr6nuX/jkEz7GfoWTmCocfPi5pchUaUtrKh1nwVELIcDv5raS38Tt9vNzvS9gL9TpWCF7BSkO31QMbiE7C72MKRREr5XoQewIHYMt5FTazLVMbV76Bx4B55fWJXbJ+Tw5z3QA4AHsSj8JGiHeBdSWNwlEHW5a6XR4RD8uhrqvUPTD18J3uVMI6wSjgqmJgbDQyDMQyyIdeC3lB9h7YQ3werud79CCdVcKKIeVzeI6ZHberpfD8sAUOJW12mNVQbAA6oWxPz4A5Bct5jWzMtAarKqvgWzhEdiAefwGGSponIppphPkZcJGkO0tZ9sD9g4gYW2TBg7AhvFeRCkzHTM8DA15UuiIeiAfgAfqn4fUK5wB3yQurKy26ywX/TlClVR2F1wFcWBPeBs/ozYrL8QpsEQlsJf1a2Z5D0iYR0VX1WZ1p3ccVAySdQqt3JCLi6KIARW1g/CQWuM/Gi2NvLPXfBys9q0haIyJOw89qPzxBLFLzoAl34N3pYor1Zl6mttydha0xW0l6MSI2KsL47/BgXmVd+mgp3p7Y07F5V5evDVbG1pq2QuhGSroaOA0LN7tgQUhYyFoUGBMRl1QXhBe9fq6cMxQvON8IKzOPhddAjZnJsi6FlZ8Nym+dKC+IbsxCVudAeV1FxTDcfijnz46FtyWAscUDsDgeCybj0JOTJX0MT3gDKkt2uf5IYGx4wX21b0gXWdHPxpPnLdjLeDtWDnfB4aR7YiX8RRz+hxxa+gfcp3cEbo+IHp2sivWvnia1BYeIPVW2R+A+8wZWVibgMfRtPB7QcG1XeijaYm7gIxFxjLxgegr2lu4PfFvSrRFTh3YWJbLdTEMR8RytC+X3wdn9lsLhfrti4fnK6LpQ1k7XIyLuwd7tFjwu/gYr+b/ABocTcMaoam3hVtg7WF8/dgbNEf4H4bDbM7Al+U1JKsLwXNhAtTNuO2cBT0naFBuMPi4vDF8JWEbSyxGxZe3e3aKETY9wdrRVO3NuKddZ+FmvHREvTeeS7mIUnVPCbsRGxn9iQ+XpeGy7C895p9NkIuI6TZvi/HZJlSIzkKk9vU/heQBc5r/Rmnnww7GoGPouw8rOu7ifXYTDyMFGyqaOXeF1YY1htkNrx7dvOH90G/cYQxdnaO0MqcB0TIcemCJwbQR8JSIeL4LRb2hdz3A5HkwnlPO7U/Cvs6uklXFH3x9PSAeW8lVxmh0KUKWjTYmID8oAvWrt2HV4gdqj7V0/g7SUZ9tSynkAtlwMkfORfxoLLYvgNT33YkVkSTlefiRlQCyDzo7AOjjLVOVtuqMop8vjsK1nsND9Gn4fN2JrRMVWOOPZw11UxzpHYivIfrV9e9LggcED+lXy4tk1sbBxLl6UeT8OHWo6EfGgnAJ8EtNOPOsAE8skebRa88VfjEP6XqmUl/JuJlRCTkREEVqHRcQzReH+h6QHcPjKDH+jJCIewCFoG9Z2tzCtB6aeHOCE6XhgJuLnvV/pM0fI37I4P0oSBUnX4PF1UZyquJ4UYCRWTL9X/QRu22vOqjAarSk8W7Dy/xQOhVkBh0reg9MsH1L7rc1wP/szFhIek1OR/yEi3qJ3ELiPV/3vPZwN620cSrUPFiTvp2TuKiyGw4bOpbl8BFi2CL9zAT/Gnr/PYqPI8ZIOxpbM/XHI5fpYWGkzhKzsXw8buu4Lrzm5HPg7fmc/Bk6UdHxEnNTN9QBbvw8BLg4ntNkFC8ljsQJzlqSDsOJ8KPB57AEI3Df2wHPRMHkR8VXhVOuzRBnjdy/z0lD5G2XVOpbXI6LySiJpNdyu5scelb9GxIZteWC6WQlrl9K3W6KDbGdq/X7VLnj+vB8bWLaW15Y+iUOWBuJUvy+2eaMm0lklLJze9yqsLF4SEUfICT7Ox2HBLxQFtbvDXT8fDSFkNTlpk+okOfR7w0r2kBlcDKzn4f4ONkq+hZXkwbR6cK7A0SBJA6nAdExHHpijisD1Av5OyQU4W8lukm4qg/yyeKI5DaAbBP+pKErAedia9DdgjyjZMspEuAde2PgZnD65HuM4GYfAVduDcaai2yTdjdtOlTZ3afxtmer6AdiKO7NpDYdgV/EFwBVFOfwIntQn4bCpJ/Fi/7vKNa8Br0k6Hlv+q2c6G/Dztlyw4cWZj0g6ipJtLiK+13heYVXsdehSJK2F43nH4OcLfranYQVlcznRwCHYY7QyFqBPxAtKwSESXwa+K+mMmLXvpXSmzMKZ9SpFvM78jadDqxVH0oZqDXXbCwtM3y3HRuBJ6ehyzTM4RnhznM1vVhhMq7dxMNN6YNaXdDq2PH3Q4IEZCsxXPC3PhLP7NE4oA2r3JyJeqx2bKhtfUUD/HNOuk+kS5PCLObDychN+Bx8H3o6IHeQPIn5R0gI4Zn8yFnCWD3+faiE8NpyGDSy9gRZclkr4nAuHhS6Bx4lHceal0fWLirGpSzJ0dUSUjGLlNxfGYW07FiHzFHkh/rY4jPHs8Dqj6vzBMW0IWZU1bQTOFlmFww4DhhSr6dfLuNhlH03ubD0i4lI5LfjutfmrKtuTWEFfHK+jeafMMRvidSJnlPr9MiKqkJwuRV5r+i42TBxFWZ+HvfYDsEFqFK1rCZ/ABr6V5TUwy2APzBSsgJ1CNyph02E14MwGo0gjg3EY4HbAJhHxz9JXtsXRIyOwQWMwrvdPm1vkqZlBJWxurGS9JH8AdS687vACHBq8JZa1TmlysauMlhU3N3hg3sMeoYNr+8Hy122SqrmyBcsbu+M5/QvYOHkoNqZdi40by2DvzJ1NqU0/INMot0MRpi7uwAPzDnYl3wWsGxH/lvQgzkayFHBARGxThKPbI+LCdgT/57ElBGZd8G+rHvMBb/SAdWKWkTSwowGuvyB/RPT7OHRjVzk71BkxdZrC1fGgdioe4C7AcbNn4TU9Y3Doxj8i4hqaiJym9GTadm2PBK6OiO+Xc9cBvlFTYH6A+4hwP9gv/CHXo7EgcFjUMp7IIYKb4A/N3cxMIi9enRhePzQMC/M9liK4u2nLQlnaXUT3fTBthigC9FUR8anSJy4Kr4eZDSsC1+IwqnXC6eDfZdoFr4vhlPdd/o2t6ZR9WES82bCvJ6zEs0RfrEcZc47FCtP7eK3UZkWIPwIrIVOwYvNqzag3DLimrTm/HJ+j3HNfHA67Ic4s2hQlrD9T5rMz6fhj2YNxkoWD8Lxwh+zZXpSp09HPjcMVD2vjHl2GnHHvxnBa9zFMu4j/6rohonbd3cD60cb61GJIHgecGs68ehZOVnONpEVw9Mir3T1+9RVSgZlFJI2MiGfL3x8O9pXwXawF4/8bBPGke5EXZs7Wi8J8Zpr2+kmZNN7O/vPfR7HSztvgzaqOTWPckDRH9PDC5KR3oNo3RCTNW/d4JX2LuozVW5A0e281/Pw3kQpMkiRJkiRJkiR9hpbpn5IkSZIkSZIkSdI7SAUmSZIk6XdImqckNEiSJEn6GanAJEmSJH0SSYPL4mskPSWnzK34IU4hmyRJkvQz0jqVJEmS9CokPYy/jzCp4dACwGcjYmzZXgxnglwEZ5waX9JEb4VTxb4mf58EnL74xKYXPkmSJGk66YFJkiRJehtvARtExIj6P+BuplZq3qc1FWuUf8OBY4D5gMERsTxO2Tqs20qfJEmSNJX0wCRJkiS9DXVwrJ468wP8TY86k/GHOyfgj5JuCCwP/LtLS5gkSZL0GKnAJEmSJL2NocBNktoKIRtc2w5gEUmPYY/LPcDNwEfx17knAVvgr2GnApMkSdJPSAUmSZIk6VVExCqdPFXAvyJihQ93SKcB5+EQ6Sci4lBJ3wbm7fqSJkmSJD1BKjBJkiRJr0DS1cDo2q7AXpfLgPUaTl8PeLmN2wwE/gdYEHhZ0pHAWsCDXVzcJEmSpIfIRfxJkiRJb2EwsGlEzBMR8wATImJy2b9Fbf9DeO3LnMAISc9Lek/S5sBJWFkZCtwPLAqcAJzbA/VJkiRJmkAqMEmSJElvIRq2P2hnP1iBWQx4uGQouwFnJRsAbAxsArwEzBYRtwLPSuooOUCSJEnSR8gQsiRJkqS30KioDGxnf8WKwBMN11+APTY3AvMAc0p6FBiCw87GdVlpkyRJkh4hFZgkSZKkt9AC3CCp8rzMXbwmLcB1kqpvvgwt+zYGLq5dOygiVqtuJmlnYHRE7NwNZU+SJEm6iQwhS5IkSXoLg4CNamtdnsPelEFMvTbmYGA8/tbLFbVrBzfcb0j5lyRJkvQjFNGeZz5JkiRJ+gaS5gQmRkTjt2OSJEmSfkYqMEmSJEmSJEmS9BkyhCxJkiRJkiRJkj5DKjBJkiRJkiRJkvQZUoFJkiRJkiRJkqTPkApMkiRJkiRJkiR9hlRgkiRJkiRJkiTpM6QCkyRJkiRJkiRJn+H/AYfw6W4yrXrdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_data=df[df.gender=='是']\n",
    "gender_data1=gender_data[(gender_data.first_type=='护肤品')|(gender_data.first_type=='化妆品')]\n",
    "\n",
    "plt.figure(figsize=(12,6),dpi=80)\n",
    "sns.barplot(x='店名',y='sale_count',hue='first_type',data=gender_data1,saturation=0.8,ci=0)\n",
    "plt.xlabel('品牌',fontsize=12)\n",
    "plt.ylabel('销量',fontsize=12)\n",
    "plt.title('各品牌男士护肤品和化妆品销量统计',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H0VS6dgHeV1U/GrlXkyRJkjRRjYnQleSZwJZV9fI2FE2rqruTfAf4Ak14eidwclVd017zaWB+VV3V716hGaL4LeBEYDpwRlUdOEKvI0mSJEn3SlWNdh+A5suRq2pZu71KVf1jtPqy9YyZ9fW5c0fr8ZIkSZJ6bHHZpaPdhQEluaCqth+q3ZiY0wXQF7ja7VELXJIkSZI0nMbE8MKxZsbWW7HFokWj3Q1JkiRJK4ExU+mSJEmSpJWRoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6tCU0e7AWHTJjZewzRe3Ge1urJQuftnFo90FSZIkaURZ6ZIkSZKkDhm6JEmSJKlDYy50JZmWJKPdD0mSJEkaDiMSupL8X/v7mUmuSnJW+/OXJJv3a34S8NwVuOchSV4xwPEZSdZNsnaSC9vfc5Kc126vm2T68LyZJEmSJC1fpwtpJJkFrAYsTTIHmAZ8oaoWtOc/A7w7ydbAjT2XviXJW9rtxwIvqaofJZlSVUvb40uAvm16zm0GvBa4G1gPWNA22bDdngR8Arh0eN9WkiRJkh6o69ULnwkcCTweOA04F/hbvzbLgHe05/v3ZynwTmBxkjWBbyVZAswAdgRIcmDbbmqSF1XVr5J8CjgWWB/oq6Rt2G6/vKquHs6XlCRJkqTBdBq6qurbSa4Afg48iSYonZhkp7bJFsDTgD8A/wP8DLijp297VdVOPbfcFSDJa4HQVK1OqKqT+z16NeC3wBv7HX8Xg7xzkvnAfICpa019cC8qSZIkSYMYie/p2p+mEvVdmvlaJ/YbXnh3Vd3TVrCeRTNsEJpAtbT/zZKsBewL9AWtf0/y/6rqlp5mRVPV2rvf5Y9uzz1AVS0EFgLMfMzMAdtIkiRJ0oPV9ZyudYAdgMtohhD+C3Bgkt3aJpsB7+m55HlVdUN77QyaIYe995sOfLm915bAYuCDNMMOX9h3LTAVmAvsxv2tTzOvTJIkSZJGRNeVri2BtwMfraqLkqwCTO1X6er1zbbiBf1WVkyyMXAicBZwNLAmzXywW4HTgfOTPB+4uKrOBDZurzsUWFRVZ7X7k5JMraolSJIkSVLHup7T9ROAnu/dmsQDK13v67lkn+VUupYBH6uqr7fnXwssrqrPtvsnA78D/iPJvJ7r5gL7Jrmp3Z8MfAM4YTjeUZIkSZKWZyTmdAHMan8PtGT8AW0I2wQ4JUnfPK4AmyY5E/hlVb0ZuKrnnpPpGSpYVZe3m8e2P7TPOAI4t61+SZIkSdKISlX3a0YkeURV3ZxkKs3wwjuGvGgUzXzMzHrsgseOdjdWShe/7OLR7oIkSZI0LJJcUFXbD9VuRCpdVXVz+3sJ961OKEmSJEkrvZEaXjiubLXWVix62aLR7oYkSZKklcCkoZtIkiRJkh4qQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHDF2SJEmS1KEpo92BMenai2DB7NHuxcO34NbR7oEkSZI04VnpkiRJkqQOGbokSZIkqUNjNnQl2TDJrivQbtoKtHl6krlJdhqe3kmSJEnSihkTc7qSzAI+0HPoUuAmYAPgJ22bKUC1P+dV1Q7tsXOBJ/WcXws4FVhKEyo/DbwOeCbwuSTPrKqlI/JikiRJkia8MRG6aMLRasDBwFRgNvAaYO8kz2nbnAnsBCwDNk3yQ5qQtUmS04DJwPur6vQkbwMuAj7ZtlkVeAfwG+DYJMdX1eUj9naSJEmSJqyxErqWAlsBRwGnA88AHg88saqWtqHqWOCYtu2ewFNoQtRJwJeAqVV1d3u/NwL70QS5NwOLgM8DLwKObu8hSZIkSZ0bK6EL4NfAGsB7gUNogtHhSRYBJ1TVnUm+x319fixwG/BX4CDgfOAtSWYCOwLfogly+wNfAzYENgJ2qKo9R+ytJEmSJE1oYyl0ARwPnAb8uqrOT/Ip4Pyq+hZAVe2V5BDgD8CTaYLabGB2VX24bXNnkm3be53Ttj2Tpkq2X1UtGOjBSeYD8wEeNTvdvaEkSZKkCWWsha5DgQ8DRwBvBzYGdgB+kORVwL40c7pWBR4F3AVcBdyZZA/gpKr6IrAmcDmwLs2crj2ALYE5SeYB83qGIgJQVQuBhQDbbzC5un1NSZIkSRPFWFoyfjPg4qr6ILAoydrADcC2SWZV1QlVNQ94CXAF8HXgFGAxcFhVPbMNXACHA8f13Ps04KXAF6pq5/6BS5IkSZK6MlYqXdNpqlK7JnkazQIZzwE+BswAjk/y78C/0YSuA2hWOrwY+CJwXPt9Xa+kWWb+5vanaILlPJrK2JwkuwDvq6ofjdzrSZIkSZqoxkToqqpbaYYSApDkDcDfq+oX7f52wD7AesBeVfX3JKvSrFh4OTAvyQ7A72iGFf6cZiGNE2kC3RlVdeAIvpIkSZIkAZCqsTd9KcmU0fwC4+03mFyL5q86Wo8fPgtuHe0eSJIkSSutJBdU1fZDtRtLc7ruNZqBS5IkSZKG05gYXjjmbLAdLFg02r2QJEmStBIYk5UuSZIkSVpZGLokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA5NGe0OjEUX//lW5h7+/U7ufdUxz+7kvpIkSZLGJitdkiRJktQhQ5ckSZIkdWjMha4kU5I8YTnn5/Tbn9F9ryRJkiTpoRlzoQt4JPCRgU4kmQ38MMm0nsM/SfK8JDv0tNs0yZHt9kFJXtluH51k8w77LkmSJEn3M2ZCV5JnJjkL+AawaZKzen6e2zY7BLgC+FSSM9pjtwFnAK9IMinJdOBmYFKSnYEZwIwk2wG7ADckmTWCryZJkiRpAhtLqxc+Ajilqo7vPZjkAGDtJJsBTwN+AfwO2CzJz4Atge8DU2neZ0/gcOB/gX16brUJcAFwPHA5cESnbyNJkiRJjK3QdQ/whiQv6Hd8PeCDwEbAKcBPgR8Ae7XXXAgcDZxVVXcnmQycRjNMcZN+97qybbtjVy8hSZIkSb3GUugC+Nggla4pVXVGkuuALwHfqaqLk3yJppp1GE0YA/gO8F3gHOD5/e5/alVdBFzU/8FJ5gPzASavvs4wvpIkSZKkiWysha5BK11JAuwO/AU4LMnbgaVV9f0kawBnJjmoqi4FSPJZ4BXA44FpwCLgc4M9uKoWAgsBpq+/aQ3ze0mSJEmaoMZa6Bq00gWsCsymqWytCtwKvCvJFOBcYCbw1/aa1wAvpBl+uB4wmWZBjelJplXVx0fmdSRJkiRNdGMtdC1vTtftwDOAbwJvBj4OPB1Yn2Y44cuALwJU1XHAcXBvaFu1f5iTJEmSpJEwlkLXDAavdK0CPAU4o6ouTfJpYAvgs8A7quqXSb4GPD7JXcCJwE3AUmBdYHKSvdtbTgU+XFXfH5G3kiRJkjShpcrpS/1NX3/TWv9lH+3k3lcd8+xO7itJkiRpZCW5oKq2H6rdmPlyZEmSJElaGY2l4YVjxjYbzmaRFSlJkiRJw8BKlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHVoymh3YCy6+M+3Mvfw7z/o66465tkd9EaSJEnSeGalS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ+MydCXJEOcn9d9O8vwkm3XdN0mSJEnqNV4X0jg5yQnAn4FHAq8G3gZMA64AvtoGrHuAnyRZA9gduDLJ9VX1vFHqtyRJkqQJZtyFriTr0QStW4B9gFWB9YG9gRnACcB7gX2BxcApwEeArdtbnDLCXZYkSZI0gY270AW8EzgDWAI8DZgMbNhu/62q/pbk0cAymkrXMmAV4BXt9WuOeI8lSZIkTVjjKnQleRawJ/BFmirXL4HpwO3ApcDkJC8DDgA2Au4CNqcJXTu1t1ljkHvPB+YDTF59ne5eQpIkSdKEMq5CF3A98HrgScC7gR/2nNse+DFNFWwS8CeaSld/SbJlVf2m92BVLQQWAkxff9Ma/q5LkiRJmojGVeiqqouSrNJuL0syBziqPb0msCMwq93fCDgSeA9wd89tJgFLR6bHkiRJkia6cRW6BtC/IlVV9dskVwGfoXm/l/acnwkcU1U/HaH+SZIkSZrgxnvo2gh4Xbs9Hbg8yROB44AbgP+sqi/3NU7yCpoVDiVJkiRpRIzH0DUdmJZkKnBxVe0GkOQTwG+BPwIH0SyccUQbtPqsT7OcvCRJkiSNiPEYus4Gzq2qJcBuPcffUFXL2u0bk/wO+FxVOX9LkiRJ0qgZd6GrDVtLBji+bIB2kiRJkjSqxl3oGgnbbDibRcc8e7S7IUmSJGklMGm0OyBJkiRJKzNDlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUoSmj3YGx6Fe33cF6P/7lCrW97mnbdtwbSZIkSeOZlS5JkiRJ6pChS5IkSZI6NGqhK8nkEX7e1JF8niRJkiTBCISuJFOS/LzdXjvJx9tT706yS3v8gCSbJ5nU7j8nydHt9qSee+2X5LdJzun3c+MQfdgEuCXJo7t4R0mSJEkaTOcLaVTV0iR3Jtm2qn6Z5PdJ1gEeU1Vnt80uA44GvpDkTcBsYHaSfwIuBt7UtrsHeE9VnQiQZI322Df7npdkA+Ac4Grg91V1MPBm4MT29xs6fmVJkiRJulenoSvJI4GnAwUsSHI3sAHw3Pb8D4EDgOuBV1fV34DvJtkD+OeqWtDvlpPb674JHAS8GLgbWNbTZgnwC+BQ4Ogk2wNPAp4CnJpkn6r6JpIkSZI0ArqudO1IG5SAlwDHAIdV1VUASb4PvKg9d1KSnYA12vaTk1wJ/AX4r6o6CZgBLAbeDSxs9/cFnt/zzOrZntm2eylwKk1QOz3JnVX1P70dTTIfmA8wad31H/aLS5IkSRJ0H7r+AvwO2K+qbksyDTglyeL2/JKq+kSS22gC0v7t8XuqqpKcAhwOXNMeXwW4qR2m+CdgVlXdlSSDPP8O4D+AdYCzge2Afwcu6N+wqhbSBDSmbrZl9T8vSZIkSQ9FpwtpVNVPquranv1X0gSeI4CrquoZvc1phhqeA5yT5GfAPJr5Wu9q22wGXJFkLvB4YPMkc4Dbk8zuudduwMnt9iRgbZo5Xm8BNq+qe4bxNSVJkiRpUJ0vpDGARwHXDnLu/wG/AtatqtP6Kl19wxGBzYEApwD7AZsA7wCuAp6c5Dc0c7zOopnTdUxVnQGQZBXgyPZaSZIkSRoRIxW6+paC356m4lTAlCSbAjsBqwKvollk46PAYcBpvTdI8gTgTuD3wAur6mrgqiR/BLYAjqWpkr2PJnjdDHyp5xavAr5aVXd39I6SJEmS9ACdh64kU4BZ7XdkfYRm5cI7gCcDPwAOAaYDH62qz7SX7Z/kDcDuwO3tsXWB91XVnTTBDYCq+n2SP9AMKVxYVX+mWTiD9v59Hk9T/ZIkSZKkEZOqkVszIsnkFZ1PlWQjYGlVXddxtx5g6mZb1lrHf2WF2l73tG077o0kSZKksSjJBVW1/VDtRnRO14NZwKKqrhm6lSRJkiSNbaOxkMaY94TVZrHICpYkSZKkYdDpkvGSJEmSNNEZuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDk0Z7Q6MRbfddjFnnLnJoOfn7f6HEeyNJEmSpPHMSpckSZIkdcjQJUmSJEkdGrOhK8mGSXZdgXbPTbLqCrSbNjw9kyRJkqQVNybmdCWZBXyg59ClwE3ABsBP2jaPAWa35ycDF7e/3w9s03OvKUC1P+dV1Q7tsXOBJ/Wdr6p7On0pSZIkSWKMhC6aittqwMHAVJpw9Rpg7yTPaducCawB7Ap8B3gK8JL22OlJAI4C1gZeASwDNk3yQ5oAtkmS07gvqJ0+Im8mSZIkaUIbK6FrKbAVTWg6HXgG8HjgiVW1tA1LxwKPBtasqqOSHAYcW1UP7HjKAAAdTUlEQVTfAEiyoD33tSTfbu+5J004+w1wEvAlYGpV3T2ibydJkiRpwhoroQvg1zRVq/cCh9CEpsOTLAJOoBlquCuwYZJn01SyBnMq973bY4HbgL8CBwHnA2/pf0GS+cB8gDlzxtLHIkmSJGk8G2vp4njgNODXVXV+kk8B51fVt5I8EXgUsDrwmLb9O5O8ut3eGDgMoKr2SnII8AfgyTSBbjYwu6o+PNCDq2ohsBBgs82mVydvJ0mSJGnCGWuh61Dgw8ARwNtpgtQOwA+q6sIkdwAbVtUnkxwKHNVveCFJXgXsS1MJW5UmqN0FXAXcmWQP4KSq+uJIvpgkSZKkiWksha7NgFOr6oNJ9k6yNnADsG2SWVV1R9tu9XZ44dSBblJVJwAnJFkP+BBwHnAHsC1wWFVd0vmbSJIkSVJrrISu6cC6wK5Jnkaz8MVzgI8BM4Djk/wZ2B+4lmZu1w0MMLywXX7+xTQrGx5AsyLixcAXgePa7+t6ZVVdNlIvJ0mSJGniGhOhq6pupQlNACR5A/D3qvpFu78dzVyubarqtvbY23jg8MLJNO+0HrBXVf29/eLkqVV1OTAvyQ7A70bs5SRJkiRNaKkae2tGJJlSVUtH6/mbbTa9jvvURoOen7f7H0awN5IkSZLGoiQXVNX2Q7WbNBKdebBGM3BJkiRJ0nAaE8MLx5rVVtuGebsvGu1uSJIkSVoJjMlKlyRJkiStLAxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHXpYoSvJ/OHqiCRJkiStjIYMXUl+keSyJH9Mcm2SPyU5pD19UMf9GxXXXnstCxYsYMGCBaPdFUmSJEnj3JQVaJOq2jzJi4HHAmcBuyWZB8xMsgswrapO77CfkiRJkjQuLTd0JXlS+/s44FHAYuAqYD3gNe3vfwemAYYuSZIkSepn0NCVZDZwDLAVcCiwF7AdcCAwHdgH+FpV7d99NyVJkiRpfBp0TldV3VpVzwAuASYDS4BrgO8CVwLfBmYOV0eSvDTJjHb7WUleN0CbOUn+td1+S5JXDHKvTZMc2W4flOSV7fbRSTYfrj5LkiRJ0lBWZPXC7wAvAFanGUa4KfA5YD5w4zD25XrgK0k2Bo4G/i3JeUnOT9I3dHEe8OR2ewmwtP9NkkwHbgYmJdkZmAHMSLIdsAtwQ5JZw9hvSZIkSRrUUHO6ngH8FCiawPWBqnpikoOBxwBfa5eNnwocX1X3PNSOVNVpSQKsSTN08RXAI4H3cl84/DdggyRPATYCliY5sH3+l6rqeGBP4HDgf9v79NkEuAA4HrgcOOKh9lWSJEmSVtRQqxe+GLiTJnQF6AtV/wkc1x6bRBN68lA7kWQmzfyx64D/A14E/AU4DXgXcG2SrwBTqqpvcY83ArdU1Rf63W5ye90jaYJWrytpqmg7PtS+SpIkSdKDsdzQVVUHJtkTuLs99LQkuwPLgDOBK6rq1mHox13AD4DDgJfSDFtcCjy3Pf95YFXgyCST+1fUkkypqr6hht+hmXd2DvD8fs85taouAi7q34G2YjcfYPbs2cPwSpIkSZI09PDCNWiG9C2mCVpnt/vnA+8HHpfkVuCTVfXph9qJqlqWZBFwXVX1fQfY7VV1XpIFwJKq+kmSycA5Se7i/sMLFwN7tPda2vb9szRDFB9PMzRyEc1ctMH6sBBYCLDBBhvUQ30XSZIkSeo1VKXrFuAly2uT5HE0X5o8nH05jKaqtQNwLXBH2597gKe0zx1seCFJXgO8kGY45Ho0Qw5nANOTTKuqjw9DfyVJkiRpSEPN6RpQkjuBP9JUvzYCrgb+52H25e/ACUm+DPwQ+BPNPK/NgM+1lTAGW6wjyRTgnmocRzPnjCQHAKu2i2xIkiRJ0ogaMnQl2YVmTleAu6rqQuCqqtqiPX9pVW31cDrRLuH+GZrvBDuyqv7QnnppkvVoFtbYDvhQkiX9rj2g3ZwOvKy914nATTTzwtYFJifZu203FfhwVX3/4fRZkiRJklbEilS6vkWzOEWAJwBPpFnNsM/Dnv9UVXcA+w1y7jrgY+3urit4yyc+3D5JkiRJ0nBYkdD1p6o6CCDJhe2xNZIcRBPE1khyUFUNukiFJEmSJE1UKxK6Bqpk/RewFs2cro/QLFax0thggw1YsGDBaHdDkiRJ0kpgRULXQF96fA3wj3Z7xgreR5IkSZImnEkr0GagStcHgI2BTYAPAmsMZ6ckSZIkaWWxIhWqTZL8D/eveN1UVR8ASHJgVX20k95JkiRJ0ji3IqFrJ+B2mtDV135YVy+UJEmSpJXVkKGrqi4e4PCsJJfRLKQxOckVVbXxsPdOkiRJksa5h7QARlXNHeZ+SJIkSdJKaUUW0pAkSZIkPUSGLkmSJEnqkKFLkiRJkjpk6BrA3X++nWsO/+lod0OSJEnSSsDQJUmSJEkdMnRJkiRJUofGfehK8vQkc5PsNMj5jXq2Nx25nkmSJEnSOAtdSeYkOSfJWUnOTvIS4GjgZuCoJFOSTEry4bZtgL2SHJhkXeAjSR7Sd5NJkiRJ0kMxrkJXVV0PvA3YC7gCKGBV4B3Ab4BjgU2B7wEHAu8BngvMB84EApyWZPZI912SJEnSxDQeqz5vBPYDVgPeDCwCPg+8iKbqtTZwWVWdmWQzYAmwAbAh8PP23K2j0XFJkiRJE8+4Cl1JZgI7At8CtgL2B75GE6g2AnYADgGOS/Jm4H3AMpogNrM9v3OSP1bVopF/A0mSJEkTzbgKXVV1Z5JtgeOBc4A/0AwbPAbYr6oWACR5VlUV8Jx2/8XAulX14cHunWQ+zTBENlx93S5fQ5IkSdIEMq5CV2tN4HJgXZo5XXsAWwJzkswD5gG7JJkD/AT4EM3ctTuTPBU4sKrm979pVS0EFgI8fv3NayReRJIkSdLKb1wtpNE6HDiuZ/804KXAF6pq56q6GzgYuAz4ArAW8A2aqtdRwGEj2ltJkiRJE9q4Cl1JdqdZHv5mmirXJJrK1nHAC5KcmeQNwLSqupBm0Y07uC+A3Q7sl2TWKHRfkiRJ0gQ0rkIX8GPgrcBXgR8C04Ezqmr3qtq6qnYHrgI+keQZNFWxVwIfAC4FXgDMArYYhb5LkiRJmoDG1ZyudnGMxcCePYcP7NfmOz27PwJI8uyquqc9NuhiGpIkSZI03MZbpesh6QlckiRJkjSiJkToerCmbbgqGx2z82h3Q5IkSdJKwNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQJUmSJEkdMnRJkiRJUocMXZIkSZLUIUOXJEmSJHXI0CVJkiRJHTJ0DeCvV/yeD71or9HuhiRJkqSVgKFLkiRJkjpk6JIkSZKkDq20oSvJGknSbs8Z7f5IkiRJmpjGVOhK8pEkmwxwfFKSVZKsm2TtJBe2v+ckOa/dXjfJ9J7LPgvskmQmcEqS1UbsRSRJkiSpNWW0O9AnySxge+D5SZ4NzAI2An5LEw6/CWwF3A2sByxoL92w3Z4EfAK4NMmLgd2BtYDJwNrAd9vCF1W12wi8kiRJkiSNndAFvBz4aVUdCxyb5F+Ax1XVR/saJNkOOBZYH9i8Pbxhu/3yqro6yfbA4cAlwPeA1YB92u0pwLdH6H0kSZIkaWyEriSPoAlKn02yOXAG8Lv23N7AKlW1A02A+i3wxn63eBf3vcuvgf2A7wI7AdNpKl47AVOBq4HLunwfSZIkSeozJkIX8GLg48AqwGLgW1X1ur6TSc5qN4umqrV3v+sf3Z6jqhYnuYsmXJ1OE9Q2aLcBpg3UgSTzgfkAj5g182G/kCRJkiTBGAldVfXJdlhg3zcS/2uSrXua9KWgqcBcYLd+t1if+4epO4Fv0VS9dgZObbcBlgzSh4XAQoBHrrlGPZT3kCRJkqT+xkToGsADKl1JplTVmcDG7bFDgUVVdVa7PynJVGAz4DhgGfBaYA3gHuAg4EpgapJDqurCkXwhSZIkSRPTWApdU4C0289r53b1mQm8Kcm8nmNzgX2T3NTuTwa+UVUnJHkJzbyubYGzaIYebt3e52Sc0yVJkiRphIyl0DWVpj9Tge/0q3T9rG9Vw55jRwDnttUveo5vTDNM8HjgA8CBNAtxvL4Ncq8EbgJ+2e3rSJIkSRKkamxNX0rzZVqTq2rpaPXhkWuuUW98xk78x1e/N1pdkCRJkjTGJbmgqrYfqt1YqnQBUE0KHLXAJUmSJEnDadJod0CSJEmSVmaGrgGsu/FjHVooSZIkaVgYuiRJkiSpQ4YuSZIkSeqQoUuSJEmSOmTokiRJkqQOGbokSZIkqUOGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhq4BXP/H20a7C5IkSZJWEoYuSZIkSerQuAxdSab32189ydQhrpnZba8kSZIk6YHGZegC3pzkHT37bwde39sgyQ5JPttz6GdJVhuR3kmSJElSa8pod+Ahej9wcpLHAHcB/wTsDpDkYOBlwCxggyRntdc8Fvh+EoAvV9XCke60JEmSpIln3IWuJKcA6wMFfB5YFwhwRpJbqmpv4LNJtgUOAH4D/BT4BPDCqnKVDEmSJEkjZtyFrqrab6g2SaYAv6qqXybZDnhSVe3Rfe8kSZIk6f7GXehKMglIVd2znOMfArZJsqzn/EdpqmJvq6pjBrjvfGA+wCNWndPhG0iSJEmaSFJVo92HByXJrsD7gLv7nZoMHFlVZ/Zr/2TgcOAG4INV9duhnvGodTarP/3t8mHqsSRJkqSVUZILqmr7odqNu0pXVf0EeOpQ7ZI8FXgncDXwxqr6U9d9kyRJkqT+xlXoGmxoYc/5qcAy4Ajg6cBqwNnAOkluBv4BTAVmVdXNI9NrSZIkSRPZuApdNBWu9yUZMHQB04Djga2BpwHTgRfQfIfXxsAaNO98BHBq572VJEmSNOGNuzldI8E5XZIkSZKGsqJzuiaNRGckSZIkaaIydEmSJElShwxdA5jz6NVGuwuSJEmSVhKGLkmSJEnqkKFLkiRJkjpk6JIkSZKkDhm6JEmSJKlDhi5JkiRJ6pChS5IkSZI6ZOiSJEmSpA4ZuiRJkiSpQ4YuSZIkSeqQoWsAi399yWh3QZIkSdJKwtAlSZIkSR0ydEmSJElShwxdkiRJktShKaPdgQcjyYuAlwHLgCcCFwLTgQ2BK4CpwEeAVYD/rqolSfYGnlRV7xidXkuSJEmayFJVo92HByXJZOCFwD8BXwWuA/YBLqiqs9o2Lwb2Bd4EfJOmorcqcC3wCGCbWs6Lbz1jZv168Z0dvoUkSZKk8S7JBVW1/VDtxlWlq/UB4OdAAXsAtwOXAe9L8l7gT1V1UpKrgc2A5wFvadsfDay/vMAlSZIkScNpXIWuJC8EbgGeDGwHPIZmqOH/AAuBg4D/SPJqYAdgEfAi4AzgL8ApwG+SfKyqftvv3vOB+QDrTxlXH4skSZKkMWxcpYuq+nqS/YHnAocDa9IMHXwl8E5gW+DPwFLgFcBuwOpV9SWAJGcBuwPXDHDvhTTBja1nzLQSJkmSJGlYjKvQlWRL4BjgE8AsYA1gMk2I2hv4B/BS4AXt/obAI5LsDMwEFgPTgI2Az410/yVJkiRNPOMqdAG70FS01qGZq7UDTYiaB/wEeE9VfRP4dJLHAqfShK/ZNItq/KvzuSRJkiSNpHH1PV1VdTxwSbs7B3gWcA/wepqhhjf3tP098AbgZOBYmoU0Dk6y7kj2WZIkSdLENq5CV2sGzVDBc4A7aIYJbkUzf+v5SZ6Q5JgkpwLPAfatqicD+wF30i6WIUmSJEkjYdx9T9dAkkyqqmXDdT+/p0uSJEnSUFb0e7rGY6XrAYYzcEmSJEnScFopQtdwm7H1VqPdBUmSJEkrCUOXJEmSJHXI0CVJkiRJHTJ0SZIkSVKHDF2SJEmS1CFDlyRJkiR1yNAlSZIkSR0ydEmSJElShwxdkiRJktQhQ5ckSZIkdcjQNYBLbrxktLsgSZIkaSVh6JIkSZKkDhm6JEmSJKlDYyZ0JZmcJAMcT5LJy7lm1QfxjDHzvpIkSZImhimj3YEeBwD7JbkHWKv9+S1NMPw58J6+hkl+U1VbAtsAhwAH994o+f/t3X+sX3V9x/Hni/YWulakQmlkSZEZovgDC6kFOg0dIOLihi4u4oCsm1gkOk0Wk4XBdHUb05ARGItzZG4QksHmMqMYq/xYayvUoTCdGmkWHVugFmFMEUGKl/f++J7K9fb23u/tvef7vd+e5yP5pud7zueevL/Np5+e1/2c8/nmZGADsAI4FngRsBx4Jsk7qurZ1j+NJEmSJLFAQleSFcBK4K5m10ub17YJbd4DnA/sBY5L8lngSOD4ZnsMuBb4PLAH+BLwGPBG4NmqurE5z6Ikqapq/5NJkiRJ6roFEbrozUit4fnZrHXAOPDZ5v3bgG9V1bkASW6sqo1J1gLrq+ov950oyanAR+iFs5cAxwFHJLkU+F9gCfBeerNokiRJktSqhRK69gJfAd5NL3x9HPhp8+ce4GbgBwBJLgZWNM95vQ/4kyQvB06rqpuq6n7g3CQnAdc051gKnANcVVX3DPSTSZIkSeq0BRG6quoh4LokFwIPAA8B/9psA9xRVXub2aprgVdU1XiSvcBJwB/QC2AkORHYDLwK+CBwKrAI+GvgA0mOAi6tqv+cWEOSTcAmgLGjx9r8uJIkSZI6JAvl0aYk7wB+h96s10TLgM/QC2Bn01tgYzPwfeDTwFPAJVX1aHOew4CXAUc15/sJsB24uqpOSPJS4LvTPdO19ISl9fR/PT2Pn06SJEnSoSbJfVW1dqZ2C2Kmq3F78+fklQXXAC+sqi3AliR/C5xOb2brCOBdVfXovsUxquo54NsASd4F/A2wu3m/E7i1qq5r/+NIkiRJ0gL6ni7gOeB79G4tnPh6bIq29wJnAvfTW8EQ4MNJzt/XIMkFwPKq+lZV/V9VnQDcCPywtU8gSZIkSZMspJmuM4HL2H+mayVwx4T3RwBjVfVsktuAq5tFNR4B/iLJC+jNmu0ENu77oSQfBd5AbyVESZIkSRqIBfNM13xKcnhVPTNp35Kqmvy82JR8pkuSJEnSTPp9pmsh3V44byYHrmZfX4FLkiRJkubTIRm65uqVR79y2CVIkiRJOkQYuiRJkiSpRYYuSZIkSWqRoUuSJEmSWmTokiRJkqQWGbokSZIkqUWGLkmSJElqkaFLkiRJklpk6JIkSZKkFhm6JEmSJKlFhi5JkiRJapGhS5IkSZJaZOiSJEmSpBaNZOhKcvik90cmGZvhZ5a2W5UkSZIk7W8kQxfw+0n+aML7K4H3ASQ5NskpSdYl2ZFkTZI1wNZm+5Qkq4ZStSRJkqTOWTzsAg7SR4FbkpwAPAOcBpzVHDsKOAlYBBwBvAl4Fvgp8HIgwE+ARwZcsyRJkqQOGrnQleRW4MVAAX8PrKIXpO5K8gPgD4FNTfOngY3N8d3Au5v97xxgyZIkSZI6bORCV1VdMN3xJOuAO4FPAs8BP66q3UmuBj4IXASMt16oJEmSJDGCoSvJYUCqavwA++9N8iTwZnqzYbvozXKdDTwFPF5VD05x3k00M2SrV69u9TNIkiRJ6o6RC13A64E/T7J30v5FwIeSPAJcCyyl9yzXeUlOAb4N/BpwzVQnraobgBsA1q5dWy3VLkmSJKljRi50VdUXgfXTtUnyW8D1wAPArfRmuP4OOAa4t+0aJUmSJGmfkVoyPslhSRZNc3ysOX4GcDe92wsPB34b+C5wMXBpkjMHUa8kSZIkjdpM13p6txYeaCGMJfRmtn69eZ1Nb0GN3c3xzfRmu/6t5TolSZIkCRix0FVVX6L3TNcBJVkC3FRVTwG3NS9JkiRJGoqRCl39qKq9wORFNiRJkiRpKEbqmS5JkiRJGjWGLkmSJElqkaFLkiRJklpk6JIkSZKkFhm6JEmSJKlFhi5JkiRJapGhS5IkSZJaZOiSJEmSpBYZuiRJkiSpRYYuSZIkSWqRoUuSJEmSWmTokiRJkqQWGbokSZIkqUWGLkmSJElqkaFLkiRJklo0UqErybIkn0ryxSQ3J7kpyeuSLE/ytSSvbtq9JMmdk352v32SJEmS1LaRCl3AxcDOqjoTeAY4ptn/MeD6qvrG0CqTJEmSpCmMWuh6GHhrkhOr6hLgUWAjsLyqPjHUyiRJkiRpCouHXcBsVNVtSZYC/5JkK7AIeD3w4ySHVdVzB3vuJJuATQCrV6+el3olSZIkaaRmupKcCHweWAOsBC4C3gN8HbhwLueuqhuqam1VrV25cuWca5UkSZIkGLHQBVwCvLWqxoFvAnuAJ4E/Bq5IMjbE2iRJkiRpP6MWuq4DNibZBqwDdgBU1XeA7fRCmSRJkiQtGKP2TNdu4FcOcGzThO0HgXMmHd9vnyRJkiS1bdRmuiRJkiRppBi6JEmSJKlFhi5JkiRJapGhS5IkSZJaZOiSJEmSpBYZuiRJkiSpRYYuSZIkSWqRoUuSJEmSWmTokiRJkqQWpaqGXcOCk+RHwK5h16GRdwzw2LCL0MizH2k+2I80H+xHmqtDsQ8dX1UrZ2q0eBCVjKBdVbV22EVotCX5qv1Ic2U/0nywH2k+2I80V13uQ95eKEmSJEktMnRJkiRJUosMXVO7YdgF6JBgP9J8sB9pPtiPNB/sR5qrzvYhF9KQJEmSpBY50yVJkiRJLTJ0SZJ0iEiyKsmOaY7/YpKHkmxrXjMucyxJs5HkhUm2JLk9yaeSLJmizeIk/zNhLHr1MGodpE6HriSfSLIzyZVzaaNum6mPdHFg0ez1cbE8luS2JHcn+d1B1qbRkGQFcBOwbJpmpwF/VlUbmtejg6lOo6Cfi+WmnddGms6FwDVVdS6wBzhvijYnA7dMGIu+MdAKh6CzoSvJbwCLquoM4JeSnHgwbdRtffaRzg0smp0+L5Z/D7ivqn4ZeFuSFwykOI2SceDtwBPTtDkduCTJ/UmuGkxZGiEzXix7baSZVNXHquqO5u1K4PtTNDsdeHOSe5sQf8h/d3BnQxewAfinZvt24HUH2UbdtoGZ+0jnBhbNWj8Xyxt4vq9tBzr55ZI6sKp6oqp+OEOzLfT60muBM5Kc3HphGhl9XixvwGsj9SHJGcCKqvryFIe/ApxTVeuAMeBXB1rcEHQ5dC0DHm62HwdWHWQbdVs/faRzA4tmp8+LZccjzYd7qupHVTUO/DvgLIX2M8PFsmORZpTkRcD1wIFuh/+Pqvpes/1VOjAWdTl0PQksbbaXM/XfRT9t1G399JHODSxqheOR5sMXkrw4yS8A5wLfHHZBWlj6uFh2LNK0mmcBPwlcXlX/fYBmNyd5TZJFwFuArw+swCHp8j+U+3h+Svw1wIMH2Ubd1k8f6dzAolY4HmlWkpyV5L2Tdm8GtgJfBj5eVbsGX5kWqj4vlh2LNJN3AqcCVzQLiH0oyZ9OavNh4Gbga8DOqrpz0EUOWme/HDnJkcAO4C7gTcAFwG9W1ZXTtDm9j1uA1CF99qNXAf8ABPhMVV0xjFq18CXZVlUbkpwFvKKq/mrCseOBzwF3AuvpjUfjQypV0iEoyWXAVTz/y8GtwJjXRtLcdTZ0wc9WDHsDsL2q9hxsG3WbfUSDkuQ4er9h/oIXOZKGxf/3pNnrdOiSJEmSpLZ1+ZkuSZIkSWqdoUuSJElS5yVZlWTHNMc3N4uDbEvyQJLL+z63txdKkiRJ6rLmWcVbgGOr6tQ+2v8z8P6qenimtuBMlyRJkiSNA28HnoCfzXptSXLP5BmtJK8FHuo3cIGhS5IkSVLHVdUTk1YGvhz4x6paD7wlydETjr2f3peI983QJUmSJEk/72XAZUm2AcuA4wCSHEXvFsTvzOZki+e9PEmSJEkabbuAT1fV1iQXAY83+88HPjfbkznTJUmSJEk/7yPAB5LcDZwHPNLsfyOwfbYnc/VCSZIkSWqRM12SJEmS1CJDlyRJkiS1yNAlSZIkSS0ydEmSJElSiwxdkiRJktQiQ5ckSZIktcjQJUmSJEkt+n+/G2HkNhUjGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,a1=plt.subplots(figsize=(14,7))\n",
    "\n",
    "gender_data2=gender_data[gender_data.first_type=='护肤品']\n",
    "\n",
    "gender_data2.sale_count.groupby(gender_data2['店名']).sum().sort_values(ascending=True).plot(kind='barh',ax=a1,width=0.6)\n",
    "a1.set_ylabel('品牌',fontsize=12)\n",
    "a1.set_title('男士护肤品销量情况统计',fontsize=14)\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=80)\n",
    "plt.grid(linestyle='-.',color='gray',axis='x',alpha=0.5)\n",
    "b1=df['sale_count'].groupby(df['日期']).sum()\n",
    "x=b1.index\n",
    "ax1=plt.subplot(111)\n",
    "ax1.plot(x,b1.values,color=\"#000F66\",label='销量')\n",
    "ax1.legend(loc='upper left')\n",
    "ax1.set_ylabel('销量',fontsize=12)\n",
    "\n",
    "ax1.set_xlabel('日期（11月）',fontsize=12)\n",
    "ax1.set_xticks(list(range(5,15)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
